Over the past few years, the term ‘finance transformation’ or ‘financial digital transformation’ has gained traction in the finance sector much the same way as ‘digital transformation’ has. If you play any role in a finance team, you’ll at least have heard of this approach, if not attempted to initiate it in your own organisation.

The problem is that where ‘finance transformation’ once had a clear, targeted meaning, the waters have now become muddied. This means that in place of the expected transparency and optimism around updating the way you work, there is often now a sense of scepticism and anxiety.

Based on our discussions with many CFOs, either they’ve tried it before, with disappointing results from the technologies or provider they worked with, or they’ve heard of negative experiences from peers and want to avoid making the same mistakes.

True finance transformation comes from a combination of process, system and cultural change. It doesn’t make good financial sense to spend thousands on a technology overhaul if your finance team cannot then use that technology. Likewise, a change in your business doesn’t count as finance transformation if your team still feels overworked, stressed and pressured at month end.

A successful finance transformation positively impacts everyone across your business on a day-to-day basis. It lowers your costs and allows you to reduce headcount, doing even more with fewer people. Not only this, but it gives you greater insights into your business, offering easy-to-use data and reporting, ultimately lowering risk to make you more compliant.

With competitors raising their game, now is the time to act.

What is finance transformation

In simple terms, finance transformation, of financial digital transformation, is the combination of process, system and cultural change across the finance operations of a business, which is then implemented through new technologies, training and analysis. As a practice, it is suitable for finance teams seeking to streamline, simplify and optimise their systems through a shift in their approach, to drive strategic value.

Many vendors claim to offer finance transformation, but what this often means is that they’ll sell you the technologies to update your internal finance systems, without the operations training that your team requires. Without enabling strategic change, this often reults is businesses might spend a lot of their budget on the latest technology without being provided with a complete roadmap to transform their systems.

In additiona, it’s easy to be persuaded by ‘quick-win solutions’ which may be more affordable in the short term, but which you will outgrow quickly. CFOs know they need to make changes, but it can feel overwhelming to initiate an “overhaul” when a simpler, more affordable solution might keep problems at bay for a while. These options regularly don’t scale with your company though, and merely tick a box to help you feel like you’re taking steps forward.

Over the long term, what many businesses don’t realise is that they are going to end up spending more money with short term solutions. Proper finance transformation is an initial investment, but over five years the way they are currently doing it works out far more expensive. Sadly, a lot of finance teams work on such a ‘hand to mouth’ way that they don’t often have time to look into better ways of working.

Feeling overwhelmed by your options

Having spoken to many CFOs, we see patterns in the things they tell us: for many, they recognise that their business needs to make a considerable investment but are reluctant to initiate it. Many businesses still rely on convoluted, outdated processes which are potentially damaging to their business – but the alternatives seem too immense to get a grip of.

Being able to predict future outcomes and planning for growth are two activities that often fall by the wayside because completing the current workload feels too all-consuming. This is a regular pattern for many organisations, many CFOs don’t know where they’ll be in the next three to five years, and instead they would like to be able to picture that. To get there, many need help accessing the appropriate technologies that are available and navigating in the right direction for their business.

How financial transformation looks practically

Some practical examples of the changes finance teams can expect from finance transformation include:

• Your finance processes and systems are standardised and automated to avoid error and increase efficiency.
• Your month-end reporting becomes automated, instead of a week or more spent creating a board pack.
• Further automation across business functions will ensure cost-saving opportunities across your department.
• Collaboration across the team becomes far easier: you’re equipped with a centralised finance data hub which enables collaboration.
• Opportunities to operate as a remote team – this means potentially lower wages and more savvy payroll planning.

Understandably, CFOs need a quick, reliable and thorough service. Their team requires training, onboarding, consultation and a ‘people element’ which is often missing from finance transformation offers – it’s a change in attitude as well as a physical process.

Want to read more?  check out our other blogs

How to get the most from your Digital Transformation journey

The growing role of Finance and Accounting in Digital Transformation and business success.

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In today’s complex global economy, transport and logistics are playing an ever-increasing role, and optimising this process is the key to success for business. In this blog we will highlight the importance of business intelligence in the logistics industry.

Improved technology has increased productivity in the supply chain and minimised costs and errors. Thanks to business intelligence tools, companies are no longer swamped with large amounts of data they don’t know what to do with. Instead, logistics managers are using BI technologies to find real meaning in their ocean of numbers—and take actions that boost supply chain efficiency and effectiveness. These advances benefit all areas of the logistics industry: trucking transportation, international transportation (ocean and air), supply chain management, and shipment tracking.

Interpreting data as an essential part in logistics processes

Transportation logistics is not any more limited only to the movement of goods across space and reducing time and costs along the supply chain. Its scope has expanded and now it is influencing decisions on what to produce, where to produce/store, in what quantities, who to choose for as the logistics provider, etc., which are parts of strategic management.

The complex and dynamic nature of logistics, along with the reliance on many moving parts that can create bottlenecks at any point in the supply chain, make logistics a perfect use case for big data and business intelligence applications. Establishing data analysis in logistics so it produces actionable insights regularly is essential in the logistics industry.

To make valuable decisions managers and supervisor need timely and accurate information and reporting environments. Vast amount of business data from disparate operating systems and applications, rapidly changing customer needs and market conditions, but also the hidden information like emails or excel spreadsheets hinder logistical efficiency. Therefore, we need integration of the core business information through an intelligent information systems and modern analytical tools to discover relevant knowledge from all of these sources, to manage uncertainty, and to create and reach our business intelligence as our main competitive advantage

A bright future…

Logistics companies are increasingly adopting business intelligence applications to manage their operations more quickly and efficiently, to further improve processes and stay ahead of the increasingly demanding market conditions.

In a survey conducted by fleetowner third party logistics companies and shipping companies both agree. 98% of 3PLs said that improved data-driven decision making is “essential to the future success of supply chain activities and processes”. Additionally, 81% of shippers and 86% of 3PLs surveyed said that using big data effectively will become “a core competency of their supply chain organisations”.

As for many other industries, data gathering and data management is becoming bigger and bigger, and professionals may need help in that matter. In any case, it looks like the future is bright for logistics companies that are willing to take advantage of better utilising their data and advanced information technology.

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Executives need access to the latest business insights to make effective strategic, financial, and operational decisions. This becomes increasingly difficult to achieve as organisational data silos grow and make it harder to collate accurate data for enterprise reporting. Combine this with competitive, fast-paced markets and the need for constant innovation, and the decision-making process becomes a time-pressured minefield that puts undue stress on senior managers.

Despite this challenge, many management teams are still going into board meetings with static reporting booklets and presentations which have been manually collated by business analysts. It leaves them without the depth of information they really need.

The problem with traditional business reporting

Traditional methods of management reporting (usually spreadsheets, static presentations, or management reporting booklets) no longer cut it in the data-driven business world.  They are extremely time-consuming to create, become outdated as soon as they are released, lack the level of detail required to explain underlying causes, and usually rely on one, or more, analysts or data specialists to produce.

This causes several problems for members of the board:

• Outdated information – as soon as sales or production figures change, static reports from the day, week, month, or even night before lack the latest information, meaning decisions are made without the latest data
• Lack of deep insights – if a further level of detail is required, such as understanding what caused the generation of X revenue in the last month, it is not instantly available in a static report. This delays decision-making or forces decisions to be made on assumptions rather than fact
• Lack of a holistic view – despite every effort, masses of departmental data sources make it extremely difficult to gain transparency overall activities. The chance of missing data is high, affecting the accuracy of reports and therefore the accuracy of decisions.
• Lengthy waits for creation – end users may lack the skills or time to analyse data and produce their own reports, creating a reliance on others within the business and increasing waiting times. Furthermore, this can often result in conflicting reports as information gets pulled from different sources, slowing down decision-making
• Reports collated from different sources – often, enterprise reports are drawn together from multiple sources of information. This generates conflicting points of view and ultimately leads to a lack of trust in the final report. The solution is to invest in a unified decision-making platform which delivers a single version of the truth to executives

To combat these issues, senior management teams are enhancing their decision-making abilities by digitally transforming their boardroom reporting approach.

The benefits of a modern business reporting solution

For the digital boardroom to function effectively, senior executives need access to real-time insights and intelligence from across the business to aid decision-making.

The self-service nature of modern end-user reporting tools can drive transformation in the boardroom. C-suite executives can move away from static reports, replacing them with interactive dashboards, presentations, and report booklets which:

• Are based on aggregated, real-time data from across the business – meaning they provide an accurate and up-to-date foundation on which to base business decisions, removing the ambiguity caused by different data sources
• Enable executives to switch between strategic and operational data instantaneously – with the ability to drill-down and drill-through data to enable a deep-dive exploration of what is driving the numbers, right down to the transactional level
• Facilitate the discovery of hidden insights – by creating transparency across departmental data and enabling cross-function analysis which can highlight correlations which may previously have gone unnoticed
• Are produced and refreshed automatically at the touch of a button – making reporting instantaneous and accurate, removing the need to rely on data analysts or IT teams to deliver information, and improving business responsiveness
• Look forward as well as back – using advanced analytics and predictive technologies to simulate future trends which help to inform decisions based on solid insights

Only with access to accurate, timely information can senior executives hope to identify opportunities for growth, efficiency gains, and performance enhancement in increasingly faster timescales. What’s more, a competent enterprise reporting solution can create the foundation for Integrated Business Planning, creating a truly unified approach.

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We have talked about the importance of data and data integration in many of our recent blogs, however we have never really discussed how we do it, so we thought it was about time we did.

Over the last few years, we have been working on a project for one of our mining services clients which involves integrating data from a number of different sources (Excel spreadsheets, Cloud and On-premise applications, Machine/Vehicle Sensor Data etc.) into a centralised data warehouse which is then used for invoicing, budgeting, forecasting, reporting and analytics.

On this project we worked together with another local company, who specialise in data science, AI and machine learning. We have recently formed a closer relationship with this company which enables us, together, to provide our clients with a full End to End Business Operations System (EEBOS). At the core of the EEBOS solution are two distinct tools, Datahub and Mailbot.  These technologies specifically addresses the data integration we are talking about.  This integrated data is then used to drive the reporting, analytics and budgeting/forecasting ‘front- end’ which the user sees and interacts with.

But what exactly is Datahub and Mailbot?

Datahub

DataHub was built from the ground up to seamlessly synchronise data from the various sources and systems. Datahub’s core engine automates record matching and data translations using state-of-the-art algorithms. Data then smoothly flows between synchronised systems to ensure all updates are translated and sent where they need to be. Administrators are kept in the loop with scheduled reports, notifications of edit conflicts via email and the ability to fine-tune the automatically learned translation mappings.

DataHub’s system-specific connectors means adding new systems to synchronise is not a problem cloud-based applications, on-premises systems and real-time (sensor) feeds are all able to be synchronised. Swapping systems from one vendor to another also becomes much smoother since DataHub seamlessly takes care of all the translations and matching, allowing clients to choose best-of-breed systems for each  function of your business and avoid vendor lock-in.

Also, when you need to put together data quickly to keep operations moving, you need to use whatever is at hand – whether it’s Excel spreadsheets, reports or CSV file dumps, you work with what can get you there. But afterwards you’ve got a problem: all these different pieces need to be consolidated back together so that you can summarise and quantify what has actually been happening and determine what needs to be done next.

Need to check the raw data that’s in the data warehouse? DataHub has a fully secure web site that lets authorised users view any data in the system. You can even have it configured so that administrative staff can manage the data directly in the DataHub, letting you store and maintain supporting data that you need but isn’t in your Excel spreadsheets.

DataHub isn’t just a data warehouse, it’s also a complete data integration and synchronisation suite that lets you share data across all your business systems, from third-party software packages to cloud-based apps, corporate data warehouses and even external partner’s IT systems. Make double entry a thing of the past and bring together all your data into a cohesive unit for whole-of-business reporting. It even makes swapping out one vendor’s system for another a seamless experience, so you can choose best-of-breed products for your company.

Mailbot

Most IT solutions expect you to manually convert your data into a format and naming convention that they will accept before allowing bulk import, or worse force you to manually type the data into their product. Wouldn’t it be great if you could instead just email your spreadsheets and reports to a central repository and dashboards and summary reporting? All by nothing more than sending an email with an attachment? Some of you would have heard of the term ’email scraping’.  In essence this is exactly what the MailBot is designed to do.

MailBot is an autonomous data-crunching robot (installed as a Windows service in your IT network) that monitors one or more mailboxes for incoming data in emails (Microsoft Exchange / Office 365 is natively supported). MailBot reads any attachments and automatically processes them, converting and validating the attachment’s contents into structured data and saving it to the DataHub data warehouse. Any dashboards or reports that run off of the data warehouse will immediately pick up the new data – all triggered from a simple email and without a single change to your existing processes.

So how does it work? We know that every company’s data will reflect their unique way of operating, so a one-size-fits-all approach to data processing will never work. MailBot is designed to handle this, being easily extensible with custom connectors tailored specifically to your data formats. Hence there is no need to re-format your spreadsheets.

MailBot will detect errors in data, collate them and automatically send error reports via email to the people who need to know. Fixing the problems and re-emailing in the attachment is all that is necessary to update the DataHub warehouse.

If you make a mistake, just fix it in your spreadsheet and re-email it in – MailBot will automatically notice what is an edit of existing data versus new records and update the warehouse accordingly.

With Datahub and Mailbot you can streamline your process without having to change your process.

To find out more about how Datahub and Mailbot can help your business get in contact with us.

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Many organisations today still use spreadsheets or specialised legacy systems for budgeting and planning; however, these systems were never designed for analytics or reporting.

Those same organisations, if they are using analytics at all, are often relying on their IT to support stand-alone BI solutions.

Today, planning and analytics tools are playing an increasingly critical role in transforming the way organisations gather, store, analyse and interpret data for strategic business decision making.

In the information age we are now living in, managers must   start viewing information as an important strategic resource that if used intelligently will improve their business’s performance and competitive advantage.

The volume, variety, velocity, and veracity of data affecting the organisation’s operations and bottom line continues to increase on daily basis and not all of this data is relevant for strategic decision making.

Data is also dispersed across many ‘point solutions’ that address specific operations of the business. These systems are critical however, to get a complete and overall picture of what is happening in the business, relevant data from these various systems must be integrated into a single source of the truth which is then used for both planning, reporting and analysis.

As an example, we have a client who has a fleet of over 150 trucks and ~300 pieces of equipment and that use sophisticated tracking software and sensors to capture data about routes, speeds, cycle times, etc. A second ‘system’ built into the vehicles which captures engine performance, and a third to measure loads. The financial data on fuel, insurance and other costs associated with the vehicles are yet in a fourth system.

Meaningful data from all these systems needs to be identified, extracted, and integrated into a single complete picture.

The base data used for planning and forecasting, in many cases, is the same as the recent historical actual data used for reporting and analytics.

Managers must be able to identify and separate the relevant data from their systems and find methods on how best to  connect to data from the other systems. The next step in the process is to then “Declutter” by sorting out ‘the clean from the dirt’ and then integrate into a common “Datahub” which can then be used for both planning, reporting and analysis.

Basing decisions on information that is not correct, not current, or not complete is a sure recipe for failure. On the contrary, when the business possesses relevant facts and knows how to use them, improved product innovation, customer relations and operational excellence will be achieved.

Fully knowing and understanding full potential inherent in business planning and analytic tools is the starting point for those companies that strive to repair the broken link between business planning and forecasting and actual performance.

Investments in planning (budgeting and forecasting) and analytic tools if not well researched and planned for can actually cost the company financially, time, resources etc.

As I mentioned above, not all data is important. Remember data collection and storage can be a very costly and time-consuming exercise.

What must you do then to ensure you’re not wasting your time and other resources on non-essential data?

As a manager, you must first establish your company’s competitive parameters within its operating markets and doing so will then help you decide which information to focus on.

To successfully execute your company’s strategies, your strategy should be based on information. In other words, information should be used to as a strategic resource to determine your company strategy.

This means business analytics supporting strategy not only at the functional level but also at the strategic level. In some organisations, business analytics supports strategy performance only at the functional level, monitoring the individual function’s achievement of targets.

There is nothing wrong with this. The problem arises when there is no feedback to the strategic level.

It is therefore important to define targets based on the company’s strategy and the process is made easier if the managers have access to relevant facts that can be used to determine the kind of information relevant for the strategy development and monitoring of performance.

In organisations where business analytics tools give feedback to the strategic level and information is used as a strategic resource, if one department learns to improve its processes through the use of information, the strategy team receives the news and spreads the message throughout the organisation as best practices.

Use of information should both be a bottom-up and top-down process. For example, information should flow down from the top via strategy maps and back up via scorecards and business performance management solutions.

When there is this flow of information up and down, deviations from targets can be measured and analysed and in turn the strategy is adapted and changed to accommodate changes in the market and within the organisation itself.

Dialogue between the strategy and business analytics functions is therefore highly encouraged.

When there is dialogue, there is improved coordination of efforts which can lead to the identification of the organisation’s critical success factors, development of KPIs and the definition of who is responsible for the various KPIs.

Aligning planning and analytics and strategy and in turn linking business analytics and strategy can help provide information about which products create the business’s income over its entire lifetime and information about relevant product attributes to develop for the different customer segments.

Furthermore, investing in business intelligence and analytics and using information as a strategic resource can also provide management with the relevant information and knowledge about which business processes to strengthen and develop in relation to the company’s strategy and competitor’s strengths and activities.

End-to-End Business Operations System for Mining

Why should rolling forecasts become your new normal in budgeting

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I wonder if many people can remember when IT used to be called Data Processing, I certainly can.

The industry has certainly or seemingly gone through a lot of changes, but one thing has not changed …. it’s still all about ones and zeroes and is still about processing data.  Yes, there is a lot more of it nowadays, however thankfully technology has advanced to allow us to store, retrieve, and process it in much more efficient and timely ways, which gives it a greater level of importance.

This is a segue into this month’s blog which is yet another variation on the important subject of data and how it is fundamental to our life today and how it can either be useful or useless depending on how we use it.

Now at bi5 we don’t consider ourselves to be IT, rather we work with IT and the business and help our customers make sense, and use, of all the data their business generates. With all our consultants having at least an Engineering qualification, as well as a mix of Finance and Computer Science , we like to consider ourselves Data and Process Engineers.

But what does Data and Process Engineering mean? Well it’s not too different to the old Data Processing, however it takes advantage of the latest technologies to deliver more meaningful insights from more data.

Data is key

When most people talk about Business Intelligence these days, the focus naturally falls to the visualisations, the slick and colourful charts, graphs, geo-maps etc. However nobody really asks about what is driving the pretty visualisations. Is the data current? Is the data valid? What level of aggregation is available?  These and many other questions about the validity of the data determine the story you are being told. It is very easy to be seduced by the visuals, and they are being used more in more in the decision-making process, but they are always reliant on the data.

We were recently presenting a first draft of a PowerBI report to a customer, and upon pausing to get some feedback it was a great feeling to see one of the senior managers ask the business questions about the data that was driving the visualisations.  This manager understood the importance of the data, and that no matter how fantastic the visuals look, without ensuring the correct data is driving them they are essentially useless.

This is where our Data Engineering and Data Processing abilities help ensure the visuals are relevant. Data comes  in all shapes, sizes, types, and levels of granularity.  The key to any successful Business Intelligence implementation is reliant on both the data available, and the ability to engineer and process this data in a way the enables the level of analysis required.

Having said that, we believe there are really four key area to consider when it comes to data.

• Data Quality
• Data Integration
• Process Automation
• Data Storage & structure

Data quality

As business environments continue to become more complex and organizations leverage data available in various file formats and cloud locations, data quality and integration is absolutely crucial to ensure that your decisions are not being driven by data that is unreliable or inaccurate. Here are some things you need to know about improving data quality and integration and how it can help your organization.

What is Data Quality and Why is it Important?

Data quality can mean different things for different organizations. Some might prioritize metrics such as accuracy and consistency to measure data quality while others may focus more on reliability and completeness. Regardless of how you define the term, high-quality data enables businesses to build far more accurate projections and forecasts, anticipate and resolve operational issues.

Needless to say, when you’re working with data that hasn’t been cleaned and validated beforehand, you need to be extra cautious to ensure that the reports and analyses that come from this data are accurate and not laden with errors. By improving data quality, organisations can automate their data integration and analytics processes without worrying about data that is out of date, inaccurate, or unreliable.

Best Practices to Improve Data Quality

Data quality management should be a top priority for and organisation, it is this data that helps target and convert leads, improve customer experience, plan departmental budgets, enhance product or service offerings, and allocate resources to maximize efficiency and productivity.

Understanding data quality issues and how they can affect your business is the most important step in improving data quality. After all, you will only be able to make improvements to your data quality once you identify what the problem is and why it is important to resolve these issues for your organization.

Here are some metrics that you can use to determine the quality of your data:

• Completeness: Establishing a process to measure data completeness can help you ensure that there are no gaps in your data analysis. Data completeness needs to be measured to determine whether crucial information is missing to ensure that insights derived from this data can be used to design reliable strategies and make projections.
• Accuracy: Checking data accuracy is extremely important. A slight difference in the format of your data can render it invalid and useless. For instance, if the Date of Birth field in your employee database accepts dates in the MM/DD/YYYY format and an employee enters 13/01/1983 in the field, the data will be inaccurate and should not be processed further.
• Uniqueness: Duplicates and repetitive values can cause inconsistencies in your data pipelines. Ensure your data is unique by eradicating redundant values that can affect accuracy and reliability especially when creating intricate integration pipelines with multiple data streams.
• Up-to-date entries: Up-to-date data is essential in several scenarios including forecasting and allocating budgets. Since most companies today need to work with real-time data and create reports quickly, it is important to ensure that all data that is being collected is up-to-date to mitigate the chances of errors.

Abiding by data governance laws and regulations is absolutely essential. Failure to do so can result in fines, penalties, and harsher repercussions.

Since organizational and customer data is used by different teams in different ways, it’s best to conduct company-wide discussions to create data governance guidelines and decide how they can be implemented. These guidelines should cover every aspect of data collection and management including where and how data is stored and which personnel will be allowed to process it.

From a data quality standpoint, implementing these guidelines could mean creating automated pipelines to ensure that certain data is deleted as soon as it is processed or that data in some fields is only formatted in a particular way.

Improving data quality is pretty much a life-long process and should be treated as such. As your organization continues to source its data from different locations, it’s important to ensure that your teams do not start slacking and are always up-to-date on the latest procedures when it comes to improving data quality.

Data integration

Data integration is a common industry term referring to the requirement to combine data from multiple separate business systems into a single unified view, often called a single view of the truth. This unified view is typically stored in a central data repository known as a data warehouse.

For example, customer data integration involves the extraction of information about each individual customer from disparate business systems such as CRM, ERP, etc.  marketing, which is then combined into a single view of the customer to be used for customer service, reporting and analysis.

Data integration occurs when a variety of data sources are blended into a single database, offering users of that database efficient access to the information they need. Collecting significant amounts of data might not be much of a challenge in the modern world, but properly integrating that data remains difficult in some circumstances.

Benefits of data integration 

There is sometimes a disconnect between the management of data and the practical application of what that data can do for an organization. It is the role of data integration to bridge that gap, permitting the data to be far more useful than it was previously.

For instance, it is common for data to exist in silos – that is, in separate databases which are each focused on a specific type of customer, product, location, etc. Individually, these silos of data may not be particularly useful, but they can be quite powerful when integrated. Of course, that integration needs to happen in an efficient and logical manner for it to be beneficial.

Data integration at scale

Smaller organizations frequently fail to have their data properly integrated. In the early stages, it’s just a matter of not having the need – the organization does not collect enough data to warrant investment in data integration. As the business scales up, however, the needs change and suddenly data integration is of the highest importance for continued growth. Working on integration a bit too early as opposed to a bit too late is going to yield better results for the company moving forward.

Data Processing Automation

The world is moving towards automation. From self-driving cars to extended manufacturing assemblies producing hundreds of thousands of products every day.

Automation of business and data processes is also moving at a fast pace.  Aside from the obvious benefits of increased efficiency and reduced costs, automation can also help businesses adapt to ever-changing market demands better and enhance collaboration with both internal and external stakeholders.

Why Does Your Business Process Automation?

The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency.” – Bill Gates

Embracing automation in internal processes not only increases efficiency but also eliminates mundane task (read cutting and pasting spreadsheets), increasing employee satisfaction.

In recent times, growing businesses have also started giving a lot of thought to data warehouse automation owing to the unique benefits it offers. Unlike the traditional approach to data warehousing, which involved spending days and weeks in gathering business requirements and objectives and typing long lines of complex code, modern data warehouses are built to facilitate automation, allowing businesses to get a complete view of their data for quicker analytics without all the effort spent manually building ETL pipelines, creating physical data models, and managing updates/improvements on an ongoing basis.

Modern DWH automation tools also facilitate data integration and ETL processes with the help of built-in bidirectional connectors to popular data sources including databases, visualization tools, and cloud platforms. This eliminates the need for repetitive data mapping and manually executing projects and processes.

Data Storage & Structure

Organisations have invested vast sums to achieve a simple objective… put relevant and timely information in the hands of business people to support their ability to make more effective decisions.

But why are so many of those organisations struggling to get the most value from those investments?

Decision support systems, OLAP, data warehousing, business intelligence platforms, appliances, data visualisation, Big Data… what do they all have in common?

Data, and the way we collect, store and organise data is vital to effectively support these the new technologies.

The capabilities we have available today, unlike the advancements referenced above, free us from the technical shackles that limited us in the past, enabling us to design solutions in a way that is unconstrained by the technology available to us.

Data warehousing solutions embrace agility and efficiency by allowing users to quickly build a dimensional model that incorporates everything from Facts and Dimensions to primary and surrogate keys, allowing you to define the structure of your model while maintaining integrity. When building such dimensional models, you also have the option to leverage slowly changing dimensions, ensuring that only relevant and up-to-date data is being processed for analytics. This, ultimately, also allows businesses to ride the wave of dynamic business environments, giving them an edge over their competitors.

Game-Changing Benefits of Data Warehouse (single source of the truth)

Data warehouse automation offers businesses the following game-changing benefits

• Quick integration of data sources: Building a functional modern data warehouse will not be possible without access to the right data sources. Data warehouse automation (DWA) software often come with connectors to all major file formats, databases, and cloud applications to ensure that you can import data to your data warehouse with ease.
• Increased developer productivity: With a single source of the truth,, developers won’t have to worry about spending long hours coding ETL scripts or managing data warehouse processes manually. This allows IT teams to focus on improving performance and optimizing processes for better and quicker results.
• Faster time-to-production: Automating data warehousing allows businesses to complete the entire process from gathering and identifying business requirements to deployment of data models quicker. Data quality rules, validation checks, and other features focusing on improving data quality also allow users to track the lineage of different datasets brought into the data warehouse to ensure that nothing is out of place.
• Supercharged reporting and analytics: business users can extract analytics from accurate and cleansed data in real-time to improve their analytics and reporting. Most data warehouse solutions also allow businesses to integrate their data with visualization tools such as PowerBI to increase visibility into business processes.
• Increased transparency for data compliance: Data compliance and governance are important issues that businesses need to be mindful of when processing data. With increased transparency of processes, data warehouse automation makes it easier to monitor tasks and data pipelines, allowing businesses to remain compliant.

The post How technology has changed Data Processing in Business Intelligence appeared first on bi5.

As enterprises enter a new era ruled by data, the general understanding that organisations which use data for their strategic advantage perform better than those that don’t becomes more apparent. As a result, more companies are turning to business intelligence (BI) software to help gain a strategic advantage, shape their business strategies, improve their products and services, drive cost and process efficiencies, and eliminate pain points from across their operations.

However, simply implementing a Business Intelligence tool such as Power BI is not enough. Certain aspects which encompass the entire organisation need to be considered in order to improve the chances of a successful BI implementation.

Before we get into the detail of this blog I think it would be best if I started out with defining what we, at bi5, define as a Business Intelligence solution, or maybe what it isn’t.

Real business intelligence isn’t just a collection of pretty dashboards full of charts, graphs and maps with unknown origins. Real business intelligence is a platform that ensures that the data is current and valid. As soon as users lose faith in what is being presented all is lost. Drill-down, slice and dice and other functions are critical and this means that the data must be aggregated and organised in such a way to allow detailed analysis. Scenario planning and what-if analysis are also key functions of a real business intelligence ‘system’.

So think of Business intelligence as a platform not just a tool.

Five factors to successful Business Intelligence

Below are our top five factors which will help you ensure a successful Business Intelligence implementation.

1. Business Culture is king

Not every business is prepared or ready to embrace a software application such as a Business Intelligence platform. In many organisations the right mindset to regularly consider data and analytics for strategic decision-making is missing.  Even though companies have always gathered information about their operations, customers, products and markets, analysis and use of this data aren’t necessarily inherent or consistent. Often businesses are held back from embracing data and analytics from the top, with C-level executives having an over-reliance and misplaced confidence in their experience and ‘gut-instincts’.

Building a data-driven culture must come from the top down.  Analysts will quickly become disillusioned if the insights and recommendations provided by their analysis is continually ignored or overruled by those above them based on instincts and feelings. It will require changes to how people think and behave throughout the organisation. The data must be accessible to everyone, and everyone must understand the value of the data for their specific roles and the overall success of the business, only then will the Business Intelligence solution become part of the culture.

2. Value data as a critical asset

If the data that drives your Business Intelligence is not current or valid you are wasting your time.

The increasing dependence on technology in our daily lives has made every business a digital business. Data is collected across the entire enterprise, often in remote offices, disconnected departments, and disparate silos. This results in disconnected business planning and poorly informed decision-making.

Data needs to be viewed and treated as a significant business asset, with the requisite level of care and attention provided to such an asset. We see all too often corrections and journals done to amend erroneous data with no further foresight than the current reporting period. While this ensures you meet your reporting deadlines, if this amendment is not done correctly or completely it can have significant flow on effects, with your ability to correctly analyse this data impacted further down the line.

Once a company becomes data driven and wishes to embrace Business Intelligence, the next steps are to put in place the tools and procedures to ensure raw data is as accurate and timely so that this can be turned into actionable intelligence.

3. Implement the right technology platform.

It is known that if the technology is difficult to use or access or does not provide the functionality expected or requested, employees will likely become disconnected and won’t use it. Therefore, choosing the right Business Intelligence platform for your business has never been more important.

Whilst at their core BI tools are all about visualisations, the most important aspect to be considered when selecting the right tool is the ‘backend’, where the data is structured. Things such as data connection options, and ETL (Extract, Transform, Load) capabilities will ultimately determine the ability of the selected tool to access the required data, cleanse and integrate it as required to provide these visualisations.  There is no benefit in having the nicest looking charts if they don’t tell the whole story or show the correct or useful information.

Once the data and visualisations are ready employees don’t want to have to submit requests forms for reports, to then receive an email with static outdated spreadsheets. They want to see what is happening right now, dynamic dashboards and reports, with the information presented in an easy-to-understand way. While culture, quality data and engaged stakeholders play vital roles in a successful Business Intelligence initiative, ultimately, it is the choice of the underlying technology, and those you choose to implement it, that will make it work.

4. Analysis is key

Once implemented and your Business Intelligence platform is in use and quality data is flowing from departments and resources into the central database, the next critical process for BI success is its use within your performance analysis process, and the success of this is determined by the success of the first 3 steps.

Making business decisions based on data is a competitive advantage, and it is a competitive advantage because it is still not widely practiced. With the right culture, accurate and timely data, analytics becomes much more efficient and effective. With improved analytics you can begin to make better decisions based on better intelligence. The business outcomes of these decisions can then be tracked back to show how analytics and BI influenced these decisions, created revenue-generating opportunities, increased operational efficiencies, and improved comer service.

With the correct implementation your BI platform will become a single-source-of-the-truth for your business. It will facilitate a level of analysis that will eliminate doubt and wonder while uncovering what is really going on with your business.

5. Think broad, start small, deliver quickly

Business Intelligence tools can deliver a very quick ‘fix’ by allowing you to develop impressive dashboards very quickly. However this is a dangerous situation which can get out of control very quickly. If there is no control over the quality and the data that is behind it you will very soon find people doubting and questioning its benefit. This doesn’t mean you have to turn it into a large project but it means you should seriously consider the bigger picture and develop a foundation of data that will better provide for future requirements. Then you can use a BI tool to develop quickly.

Just like building a house, if you don’t start with a bigger plan and a solid foundation you won’t get a good outcome.

Business Intelligence and Your Bottom Line

These are our top five factors that can influence the success of your Business Intelligence implementation, and ultimately have a positive impact on your bottom line. Others which we have not discussed above, and which come later on in the process include, deciding which decision to make based on the analysis, monitoring progress of decisions made, and constantly reviewing and refining the BI tool, it’s data and visualisations and analytics provided.

As BI becomes more engrained in your business culture, it can have a dramatic impact on how you engage your customers, create and market new products and services, and develop strategies that benefit your bottom line.

Want to read more?  check out our other blogs

Unifying Planning and Analytics

Measuring Performance with Key Performance Indicators (KPIs)

5 Benefits of Business Intelligence Software

End-to-End Business Operations System for Mining

The post Four Factors that Influence Business Intelligence Success (Now five) appeared first on bi5.

November 2020

Australia is home of some of the most precious metals in the earth. Highly specialised processes and equipment are often required to extract, refine and use these precious resources. As technology evolves and becomes more sophisticated our ability to tap into these resources is improving all the time, allowing previously untapped deposits to become accessible and viable for production. 

In the world of digital technology there is a focus on extracting and using another valuable resource – ‘data’. In the digital world, ‘data’ is the new gold, ‘meaningful insights’ from data is the new platinum and ‘actionable insights’ are the most valuable of resources. Mirroring the mining world, as businesses become more sophisticated at tapping into data, they are able to go deeper and extract more valuable and actionable insights.

All well and good you say, but what does this talk of resources have to do with the CFO? Well, when it comes down to it, money is the most critical resource in any business – and who controls the money? The CFO. 

The new role of the CFO

A CFO’s traditional role has been to focus on strategy and the end-state aspects of financials. But in today’s data-rich environment, CFOs are expected to understand and use it to predict, influence and shape decisions that drive financial results.

When a business can extract meaningful insights from data, it has the knowledge to become more competitive, tap into new markets, better develop products and services, provide a better customer experience and generally increase profits.

But just like mined ore, data alone is of no particular value, it must be processed and shaped into something more ‘actionable’ before it can add value to a business. This transformation is often wrongly termed as big data, Internet of Things, predictive analytics, machine learning and artificial intelligence. A more correct reference is ‘integrated data’. 

Every business has a plethora of specialist systems that collect data from various operations and many external sources.  The data from these sources needs to be integrated, validated and current in order to get a complete and accurate picture of the financial health of a business. Companies are turning to their C-level people, specifically the CFO, to lead this operation.

So how can modern CFOs turn data into actionable insights? Here are our 7 steps to get the right data alchemy.

Step 1: Identify key success drivers

Based on our experience, there are typically 15 to 20 non-financial metrics which are drivers of enterprise performance. CFOs need to find what these performance drivers are and then ensure that the right data is continually collected, shared and used. If you are unsure or just starting your data analytics journey, use this as a starting point and progress from there.

By way of example, one of our mining contractor clients that supplies mobile crushing and haulage services to remote mine sites determined two of the most important drivers of success are cost per tonne and cost per kilometre.  When the client decided to collect and post the data daily on these two metrics to the enterprise dashboard, the enterprise’s focus completely changed.

Step 2: Look for relevant data everywhere  

Data is everywhere. CFOs must step away from their financial data lens and start to survey and analyse the different data that exists outside the chart of accounts. 

CFOs must also identify which data sources are essential for the success of their organisation. For instance, the requirements for a project-based business might require in-depth insights into every project, past and present. On the other hand, a mining company would be more interested in data that helps analyse daily production results and costs.

Most importantly, CFOs must have the ability to cross-compare multiple sources of micro and macro-level data to identify and understand correlation and causation.

Step 3: Clean your data

Unfortunately, unless it is collected by systems without any human intervention, enterprises have to live with “dirty data.”

We’ve all heard the saying “garbage in, garbage out, so it’s important we know the information is right ”  So, whether data has not been consistently recorded, is missing or hasn’t been maintained, it will likely need some refining before it can be trusted for use.

One small but critical change is to make the names of fields understandable and recognisable. Data stored in ERPs and other systems has naming conventions that will mean nothing to business users, or any users for that matter.

A disciplined approach must be used for ensuring data is scrubbed clean and is consistent across different systems. Otherwise, information and insights will be derived from misleading and incorrect information.

Step 4: Integrate data

Very few companies have all their data available in one system. We find Internal data often exists in multiple business systems and external data often arrives piecemeal. This “disconnected” data means we might have to deal with “islands of data”.

It is becoming more viable and more common for companies to create their own centralised data warehouse/data marts/data lakes where data from various systems is extracted, transformed, loaded and then linked and analysed.

CFOs have the responsibility to identify islands of data and initiate the centralisation of data for use.

Step 5: Upgrade your data visualisation

Data by itself is essentially unreadable. It has to be refined, packaged and viewable from every angle for data analysis to shine with insights.

Many companies use Excel for visualisation of data at the cost of many administrative labour hours. 

Don’t get me wrong, Excel is an excellent tool when used for what it is designed for – a personal productivity tool. Sure it can do a lot of ‘good stuff’, but this usually has to be driven by the business’s Excel guru – which that’s fine if you can chain them to the desk and they stay with your organisation indefinitely, but not really practical if you’ve got multiple people needing to make sense of information at the same time. Excel is not a scalable or sustainable solution for data delivery.

It is the CFO’s role to invest in data visualisation or business intelligence tools that are able to provide an intuitive display of historical information, and which also allow for sophisticated data analysis that will help drive strategy and action for the future.

Step 6: Drive accountability for data usage

When a business makes a significant investment in its data (as per the previous five steps), it is the responsibility of the decision-makers to use this knowledge. I hear so many stories of key executives who are reluctant to ‘take Fred’s spreadsheet away from him’ –  a classic case of the tail wagging the dog. This does not make sense.

Like any other investment, CFOs need to monitor the usage of this “data-to-insights” investment by the key decision-making people in the business. 

I typically recommend that companies start using their enterprise dashboards (data) as a wall of fame for leaders and provide full transparency by kicking off monthly, quarterly and annual performance meetings with the dashboards displayed front and centre for everyone to see. Then the business decision-makers can use the dashboards to elaborate on insights and action that helped move the needle.

The only way to convert your data into results is to make sure leaders and decision-makers are making the most from it.

Step 7: Ensure security and governance

As a valuable resource, data must be protected and governed. There are immense benefits to becoming a data-driven enterprise, but there are also risks:

Data validity and ethics – ensure only valid data is used and reasonable insights are inferred.  CFOs need to outline strict and monitored data collection and storage and interpretation methods, and ensure that all involved are held accountable to the highest standards of ethical behaviour.

Data security – prioritise data protection and privacy. Like any other precious resource, data is vulnerable to theft. When an enterprise starts to collect and mine data, there’s a risk that it may be breached or accessed by external entities that exploit it for their benefit. A CEO should take the necessary action to be able to ensure data security.

Like any other precious resource, data is vulnerable to theft. When an enterprise starts to collect and mine data, there’s a risk that the data can be breached or accessed by external entities that exploit it for their benefit.

Final thoughts

Data-driven, high-performance companies require CFOs to be on top of understanding the business model and performance/value drivers of the organisation (which can’t be found in the chart of accounts or financial statements). It requires investment in not just data collection systems, but analytics and visualisation platforms.

CFOs are stepping up to lead the data-to-insight revolution and work with other business leaders to build a solid foundation of data mining, refining and reporting that can transform already valuable information into actionable insight to drive better decision making.

The post 7 Steps to get Meaningful Insights from Data appeared first on bi5.

October 2020

As more and more people become aware of Power BI, and more broadly the Power Stack, and start to consider investment in it, we thought it a good idea to look at some of the key things we ensure our clients consider before puling the trigger on their investment.

Power Bi is an excellent product, arguably the best out there, when used for what is was designed to do, and that is visualisations. However we, and some frustrated  finance departments, are finding there a many people who are sold by the hype, and jump head first into an investment without first understanding some of the limitations. Whilst almost everything can be worked around in one way or another, sometimes these boundaries mean the technical workarounds required to achieve certain outcomes far outweigh the benefits that are achieved.

Here are a few things to consider when evaluating if Power BI is the right product for you.

Power BI is not a standalone solution

Power BI is a fantastic data visualisation tool, but many people fail to realise is that it is just the tip of the iceberg of a ‘family’ of tools that are required to store, transfer, integrate, calculate and present data from multiple data sources. Before you can use the features to build insights in Power BI, you must first develop what can quickly become complicated data models ,or create your own data warehouse.

This can take months to develop, and required specialist technical knowledge, depending on the volume of your business data and complexity of your systems. It can also be a very costly project that can degrade in value over time if not maintained correctly. Custom built data models need to be maintained and be able to  integrate new solutions or upgrade old ones.

The key to being successful with Power BI is the quality and relevancy of your data across your users.

It is not a CPM tool and not really suited for Budgeting, Forecasting and creating Financial Statements – without a lot of work-arounds

One of the more prevalent misunderstandings and the source of frustration for many CFOs, CEOs, and Finance teams across the globe is the fact that Power BI cannot be easily used to build your board-ready financial statements. This is not exactly true, in that it can be done but with a lot of work, but the bottom-line is it is not a CPM tool.

A dashboard is one thing, and something Power BI does a great job at delivering, but paginated reports are a completely different beast. Attractive visualisations help to quickly identify trends and spot anomalies, painting the bigger picture of your business operations – but this doesn’t replace your need for financials or operational details. Financial statements, receivable reports, and anything showing more than the basics will require a completely different set of skills.

Power BI lets you see the big picture, but the smaller details, itemised data and the ability to perform what-if analysis and scenario planning are crucial when trying to make decisions that impact your cash flow and revenues. Additionally, regulatory requirements, banks and lenders, and other sources need you to produce actual financial statements on a timely basis.

Power BI does not enable simple integration of multiple data sources

While adding a data source to Power BI may be a relatively simple and straightforward exercise, combining data from multiple data sources together is something much more complicated. Automating update processes to keep the data valid and current is also not a strength.

Power BI cannot easily handle data normalization for tasks like Master Data Management. When you have things like item numbers, customer numbers, GL accounts, etc. that don’t align perfectly across systems, it creates a huge challenge for those who are not intimately familiar with data modeling. Most businesses don’t employ data scientists and modelers who know how to integrate multiple data sources to effectively manage the data and ensure inputs are valuable and true. As this is a highly sophisticated discipline, it is something even programmers would struggle to manage with any degree of certainty. And this is a huge problem with Power BI – it requires the work of external experts to become the powerful tool that it can be. And that work comes at a price, both relative to time and cash.

If you’re storing data in more than one place (and who isn’t?) then having the use of pre-built connectors and a proper data warehouse is going to be the best path forward for reporting and business analytics. And your data warehouse can feed Power BI; it’s just a matter of whether you have the time and resources to build your own data warehouse.

Creating complex calculations is not that simple or fast

Something as common as a month-to-date or year-to-date calculation is not that simple in Power BI. To perform this type of calculation (and numerous other common ones), you need a date calculator – something that isn’t built into Power BI and isn’t standard. Alternatively, you can use a custom table or DAX, which you would need to build on your own.

This is another example of how Power BI is not always a quick and easy solution to implement but, instead, is a highly sophisticated set of tools that are only as good as the way it’s set up.

A seemingly simple request can take a disproportionate amount of time and effort to build; expecting the product to deliver outside of its confines will only create poor results and wasted resources.

Many EPM/CPM products now provide better and easier Power BI integration. With their data warehousing and integration, and budgeting, forecasting and financial reporting capabilities, CPM/EPM products fill in the missing pieces of the puzzle, whilst enabling you to quickly and easily integrate this data into Power BI to provide the data for your organisations dashboards and visualisations.

So before you decide to invest in Power BI, consider if it alone can provide everything your business requires.

The post Power BI : what you need to know before making an investment appeared first on bi5.

If you haven’t heard about Power BI, then it’s probably worth a Google. Power BI is one of the, if not THE, leading Business Intelligence (BI) visualisation platforms around. However it’s not ‘new’. In fact it has been around for quite a while now, however it’s only been in the last few years that corporations have started to understand the importance of having a BI solution for their businesses. And it’s been even more recently that some consultants have realised the importance of BI for their clients. Both of which have led to an explosion in Power BI articles and search results on Google.

As our name might suggest, at bi5 Solutions we understand the important of BI tools, and have done so since the early days. Rather than band-wagon jumpers, we have been involved and working with BI products of some sort since the beginning. Over the journey we’ve researched many different BI tools, and their related cousins Enterprise & Corporate Performance Management (EPM/CPM) systems. Over this time we’ve seen BI products come and go, some get bought out, and new ones appear. However none have had the weight of Microsoft behind them, and none have been as widely known as Power BI, and that’s a sure sign Power BI is here to stay.

Data is the new oil, but what is the new car?

More and more corporations are starting to understand the importance of data. Some have understood this for a long time, whilst others are a bit later to the party. However it’s never too late to start utilising and analysing your data. To do this many businesses are turning to Power BI as the most common and well-known product out there.  But few understand the importance of a ‘proper’ Power BI implementation.

We see many businesses start their Power BI journey by putting staff on a Power BI training course.  That is a great way to start scratching the surface of what Power BI can do, but Power BI is much more than individual dashboards, and there is much more to learn.  That thinking ignores, or diminishes, some of the main benefits of Power BI. With everyone building their own Power BI reports and dashboards, there can be just as much confusion caused by conflicting reported values as there was before Power BI.

Most businesses that want to head down the Power BI path do so as they have multiple and disparate data sources and systems which they want to integrate.  Often there is manual entry of data that is required to be displayed in a Power BI report, and very frequently this data is manually entered into a spreadsheet, or spreadsheets, which are then connected to the Power BI dataset.

As great as Power BI is it can only display data that already exists, that is, it is not possible to enter data into Power BI. The further down the road people go the more they ask of it, and the more it can start to replace existing Excel based dashboards and reports.  Inevitably users then start to wonder what it is capable of, can they enter data into it, and use it for budgeting and planning?

How to add more Power to your BI

Power BI is a BI tool, its even in the name, and BI tools in their pure sense are visualisation tools. That is they allow you to view your data in a visual way, think charts and graphs and maps.  But for all its useful features and high quality visualisations, it still has limitations. Some are to do with entering data as mentioned above, and others are with more simple things such as creating a P&L, a Balance Sheet, or a Cash Flow Statement. Without custom visualisations these can prove difficult in Power BI, if not impossible. We often find that Finance professionals still revert to using Excel when preparing their reports, as Power BI doesn’t give them the same abilities and flexibility to undertake the quick analysis and calculations they often require.

As great as Power BI is, these limitations prevent many from being able to fully embrace it. However, the great thing about Power BI (ie. Microsoft) is they tend to build a platform, which they continually enhance and improve, whilst also allowing other products to utilise and enhance with their own add-ons.

By utilising these other platforms in conjunction with Power BI that businesses can really enhance the benefits derived from their Power BI implementation.

For some of our projects the issue is around data-capturing or record-entry, to move away from reliance on a single spreadsheet with manual data entry. In projects such as these we can make use of the available technology within Microsoft’s Power ‘Stack’, such Power Apps, Power Automate to capture the data, make it available to Power BI, and reduce the reliance on a single spreadsheet.

Power BI Budgeting

However commonly the solution requires more than just data capturing. When discussing Power BI the topic of budgeting and forecasting often comes up.  After all, there is not much use in reporting actuals without some variance analysis to budgets of some kind.

For these situations we use a platform called Acterys.

Acterys is an all-in-one platform that leverages the high-quality front end and visualisations afforded by Power BI, and then greatly enhances its capability with its own custom Power BI visualisations and virtual data warehouse to drive it.

Through combining Power BI and Acterys, you can remove the limitations you are bound to encounter with your Power BI implementation, whilst at the same time providing you with the ability to expand your Power BI beyond the limitations of pure BI platforms and into the EPM/CPM space. For the lay-person that means use Power BI for budgeting and forecasting.

Acterys partners with Microsoft, and provides you with a virtual SQL server data warehouse in an MS Azure environment, and then provides a simple connection to Power BI where you can create your dashboard and reports.

Data from all your disparate sources are integrated within this single SQL server data warehouse. This provides you with a single source of the truth with which to drive all your reporting and planning needs, either in Power BI, Excel, or any other reporting product you wish to use.

Data Entry in Power BI

One of the most impressive features provided by Acterys is the Power BI custom visuals that come with it. These custom visuals provide the end user with the ability to enter data directly into Power BI reports, and have this data written back to your data warehouse. This means the data is then ready for use elsewhere, in other systems which are using this same datawarehouse.  With the ability to enter data, both text and numerical, it is then possible to go one step further and build a full driver-based budget and planning system, using Power BI as your front-end user interface.

Acterys also provides an Excel add-in allowing you to easily connect your datawarehouse to Excel, and provide writeback.  This allows you to enhance existing spreadsheet reports and budget models, and also provides the Finance team with the familiarity and functionality of Excel, yet still ensuring they are using the exact same data as that driving your Power BI reports.

To find out how you can, and should, be adding the Power to your Power BI implementation have a chat with us today.

The post Add more Power to Power BI – budgeting and forecasting appeared first on bi5.