We’re at a tipping point where using data to inform all types of business decisions is becoming the norm. With more and more data available to users and the increasingly competitive business landscape, it’s not surprising that the needs of business users are becoming more complex.
As more organizations turn to data analytics to deliver value, the battle between BI tools is heating up. Data discovery tools sprouted up to solve the need for speed, while traditional BI solutions were architected to deliver every possible analytic issue and one universal picture of the business. So in the battle of BI who will be crowned victor: data discovery or traditional BI?
Surprisingly, it’s not one or the other; rather there is a time and place for both.
BI has long had a reputation for being difficult to implement and equally difficult to use. Organizations would bring together a team of very capable analysts and a few data scientists who would be in charge of implementing a very powerful (and very expensive) BI product. These folks would then create highly accurate reports from corporate data that would be aimed at helping inform high-level decision-making.
But what about the people that don’t have access to the company’s core data (let alone a dedicated team of data scientists)? From this chaos, data discovery tools were born and started to gain traction. Quick, and more visually compelling than shadow BI tools, data discovery allowed individuals to overcome the hurdles of Excel and pull data from different sources within their organization (including the core data pipeline) to answer ad-hoc one-off questions.
In many ways, data discovery responded to the need for speed in today’s enterprise and was useful to data savvy users across an entire organization because it gave them an answer fast. But it isn’t architected properly to arise as champion for data problems. It still requires a data analyst to build each query from scratch — creating yet another data silo, and lacks capability to make analytics part of process instead of a one-off activity. So when both sales and marketing ask a data discovery tool “What’s my lead to close rate?” each department yields different answers.
Data discovery remains one small piece of the larger pie that is business intelligence. It’s most useful when making a fast, one-time query. But when it comes to making analytics part of a daily business process, like providing personalized context aware analytics to every sales person in a 1000 person consumer packaged goods company to enable each person to make smarter decisions every day, it fails to deliver.
This is where the opportunity for BI lies. The tradeoff between agility and complete business analytics is disappearing as new technologies bring the speed of data discovery to a full suite of BI tools that everyday business users can easily leverage in their daily lives. The complex process of bringing together different data sources throughout an organization (such as CRM and ERP) is now being automated creating a single, semantic layer of an organization’s data.
Whether you’re in sales or marketing, your question about the lead to close rate goes back to the same core data repository and definitions. When you show up to a meeting to discuss the numbers, there won’t be any confusion about the accuracy of your data and, therefore, you can confidently focus on a driving your business forward every day, with data at your fingertips for every decision.
When these complex processes of integrating your data are automated, your BI suite can deliver on the promise of democratizing data. Instead of relying on teams of analysts and data scientists to bring together your data, you can now ask your software questions in common business terms and quickly get accurate answers. When the CEO and the sales manager can ask the same question and get the same answer, you are empowering everyone to run a data driven business.
Southard Jones is VP of Product Strategy at Birst.
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