The emergence of data blending and why business analysts are bypassing IT to do it

In our connected world, data is here to stay. Spilling over from almost every aspect of our lives, data is brewed in the websites we visit, the retailers we shop at and conversations we have on social media.

As such, we are at a stage where the thirst for data by companies to better understand their customers and optimise their supply chain has reached the boiling point.

The importance of data in business isn’t new. Almost every company in the world has used some form of data to prospect and retarget customers. However, technological advances over the past decade have created a quicker, more frantic world, where the faster a company can access and use data, the bigger advantage they have over their competitors.

Data is still in the hands of the IT gatekeepers when it comes to data flowing in and out of departments. But it is the analysts that understand the business and understand what data is important to their business.

They are findings spreadsheets, manual processes, custom scripting or working with data specialist or IT to be too time consuming or just not powerful enough to handle their needs.

With the emergence of data blending software, the process of combining data from multiple sources to create an actionable analytic dataset has become possible.

Data blending differs from integration and warehousing by keeping the data completely separate and only combining it to answer a specific business question.

By combining relevant data on common fields, blending in the specific information an analyst is looking for, they can understand what products or services are having the biggest impact on sales and what is driving the interest of prospective buyers.

>See also: How Bolton Wanderers are revolutionising the use of data analysis in football to win back their Premier League place

Where the software can have a massive impact on an organisation is in the repeatability of its projects. Once a process has been created, analysts easily can run the same query with updated data at any point.

In an age where dynamic, flexible and adaptable businesses have an edge, it would be easy to believe that data blending holds the key to success. Like anything, however, a chain is only as strong as its weakest link, and in this case it is the data that is fed into it.

Such is the necessity of having quality data, we are reaching a point where the financial burden of poor, unrefined data can end up costing a company more money than is being saved by not investing in better systems and processes to capture it.

While traditional data analysts use traditional IT tools to generate reports on historic data, today’s analysts have the chance to extend that capability with business insight and predictive capabilities to find the information their organisation really needs in hours, versus the weeks of traditional approaches.

With improvements in information technology and the constant influx of big data, a flood of new opportunities for business insight has appeared.

Empowered by next generation tools, today’s analysts can now do what previous generations of analysts could only dream of.

These analysts are able to quickly perform data blending to create the analytic dataset they need to deliver the deeper business insight they require.


Sourced from Matthew Madden, Alteryx

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Ben Rossi

Ben was Vitesse Media's editorial director, leading content creation and editorial strategy across all Vitesse products, including its market-leading B2B and consumer magazines, websites, research and...

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