Data silo busting

lan610 data silo busting

“Information silos are the scourge whose downfall has been foretold ever since the rise of enterprise resource planning platforms in the 1990s, but today, their demise remains far from assured,” said the author of {cslimited}this ComputerWorld {/cslimited}article in May 2015.

The article continued: “On the heels of the consumerisation of enterprise software and the growing ubiquity of easy-to-use analytics tools, silos appear to be coming back in all their former collaboration-stifling glory as individual teams and departments pick and choose different tools for different purposes and data sets without enterprise-level oversight.”

That does not sound good. But does it really matter, and if so, how much? A lot, according to Aberdeen Research, which surveyed a number of companies and attempted to correlate the relative extent of their data siloes with their relative operational performances.

Aberdeen group classified the organisations in its survey into three groups: Best-in-Class (the top 20 percent of performers); Industry Average (middle 50 percent); Laggards (bottom 30 percent) based on the three metrics: improvement in organic revenue year-over-year; improvement in operating profit year-over-year; improvement in searchable/discoverable data year-over-year.

You could argue that the last one is self-fulfilling: organisations with data silos are almost certain to show less improvement in searchable and discoverable data than those not so afflicted. That aside, the bottom line was that the Best-in-Class organisations were 40 percent less likely to report problems with data silos and 55 percent more likely than all others to have achieved an open exchange of data across business functions.

So how do you demolish data silos? “The best-in-class are 85 percent more likely than all others to have a dedicated data prep tool that is accessible to the line of business,” the report said. The top performers also practice the open exchange of data across business functions and have established data governance practices and tools.

According to Forrester Research, “Data preparation tools are the catalyst to bringing trust, speed, and actionable insight for all data where traditional data governance and management tools have hit the wall.”

And there’s no shortage of choice. The web site of Predictive Analytics Today lists 29 of them. It says: “The purpose of data preparation is to transform data sets in a way that the information contained is best exposed to the tool. Data preparation tools and platforms enable data discovery, exploration, analysis, conversion, cleaning, transformation, modelling, structuring, curation and cataloguing.”

If you think this description makes data preparation tools sound like data analytics on steroids, you’re not alone, but you’re mistaken, according to Forrester analyst Michele Goetz.

In a blog post she says: “It may be sexier to think of data preparation tools as big data analytic solutions. Yet, that would be missing the complete value and relevancy these tools have in the bigger picture of getting control and competency with big data for more than data science activities and limited operational implementations.

“Data preparation tools are the catalyst to bringing trust, speed, and actionable insight for all data where traditional data governance and management tools have hit the wall.”

To put it simply: they’re silo busters.

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