Self-service analytics is not just about visualization
Enterprises today are investing millions of dollars in business intelligence (BI) and analytics tools, with the market reaching $18.3 billion in 2017. Despite these tools, big data analytics in most companies still works the way traditional BI did in the past decade. Business teams outline a problem that can be solved using relevant data, and then they request IT to provide the data and quite often reports too. Normally, this is because querying huge datasets is too complex for an average business user whose knowledge of data tools is limited to Excel.
Self-service Data Preparation
Analytics tools that are “self-service” promise to utilize the flood of data that organizations have without depending on IT to capture, clean, and prepare it. However, most such tools offer self-service only to the extent of concealing SQL complexity while creating charts. IT still has to participate in the process because the data comes from multiple places. Integrating this widespread data and transforming it into a format that can be used for analysis is a laborious challenge for both business analysts and supporting IT teams.
If this is not done, businesses will be able to identify patterns based only on a single data source – for example, sales declined in quarter one – in contrast to identifying patterns comprehensively using multiple data sources – sales declined in quarter one at the Wilmington branch store when there were stock issues due to delayed shipments from distributors. To address this pain, modern analytics tools need to incorporate self-service data integration and preparation within the analytics process. Business users should be able to combine data sources on the fly in a few clicks, instead of waiting weeks for IT to bridge data silos.
Automate Analysis with Machine Learning
Another fundamental issue is that “self-service” tools require business users to invest a lot of time in combing through data manually. But it is unlikely for average users to spot complicated patterns in the data by themselves, nor should they be required to do so. Business users don’t need to go through more information and put in more work aside from their usual duties, they need tools that eliminate excess analysis legwork and automatically provide insights. Harnessing machine learning and artificial intelligence is the way out from this problem. Incorporating a library of readymade machine learning algorithms into the analytics tool so that they can be easily used by non-data scientists will bestow teams with the ability to make non-linear connections and ask new questions. Automating pattern recognition will lower the burden on business users and will ultimately lead to faster time to insight.
There are specialized products that address each of these issues separately, but what is required is a solution that can pull all these activities together into a seamless, unified process. Modern analytics platforms need to deliver self-service across data preparation, visualization, and advanced analytics – not either/or. Such a truly “self-service” analytics tool can boost the productivity of analysts by giving them complete control over the analytics process and empowering them with advanced analytics skills they would otherwise not have.