How technology has changed Data Processing in Business Intelligence

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.