You understand that data is the lifeblood of innovation and competitive differentiation. The key is to get the right data into the hands of business analysts when they need it. Sounds simple in theory, but challenges abound. However, for every challenge enterprises face surfacing and connecting the right data, there is an answer.
A splash of data used to be all you needed to get attention. Today, data-smart audiences want much more: detail, what-if scenarios, and deep, multi-sourced data for any questions they may have. They also want to see your work, from initial questions to final insights.
In a recent blog post about data exploration, Forrester's Boris Evelson discusses Tableau's recent acquisition of HyPer. He notes that HyPer addresses Tableau's previous lack of in-memory data exploration capability.
Enterprise search is back in the news—with a twist. Companies that really want to accelerate their results with BI and Big Data are looking to enterprise search as a way to help business analysts quickly find the data they need. Note that I said “data,” not information. Enterprise search has always been thought of as a way to find unstructured content in file shares like SharePoint. But now, it’s being applied to strucutured data as well. And if a search solution can combine data with unstructured content so much the better.
Recently I was thinking about the data bottleneck between BI analysts and IT that can add months to analytics initiatives before they produce any meaningful insight. It’s not just that most enterprises are saddled with legacy infrastructure for handling data. Tools like Tableau, Qlik, and Spotfire have created an order of magnitude increase in the number of data consumers in business. There just aren’t enough bodies on the IT side with the technical skills to handle the demand for data the old-fashioned way using code or ETL and MDM tools.
I’m still fascinated by the overwhelming response to this slide, which features a quote from Forrester Research, Inc., VP Boris Evelson. People universally agree, and usually even tell us that 80% is probably low. That’s a lot of time and resources being channeled into background tasks. Most organizations don’t realize that there is a far more efficient way to get data into BI tools for analysis.
You Have Self-Service Data Analytics. But Do You Have Self-Service Semantic Data Catalog?
I know that many organizations thought that desktop or cloud-based self-service data analytics tools would push their Big Data and BI initiatives from lackluster to groundbreaking. But, unfortunately, data discovery often consumes well over half the time spent on analytics projects. That can certainly put a crimp in your analytics ambitions.
I’ve written frequently about different aspects of data discovery and their importance to Big Data and Business Intelligence. And the fact is that self-service data discovery will only grow more important as Big Data sources and their various processing frameworks proliferate.