Does self-service analytics improve collaboration?

Today, business intelligence is largely centered around business users. Traditional tools, whilst available to the whole businesses, were primarily controlled by IT techies and data scientists. Those who understood the business were often limited by the technology and vice versa, in turn stifling exploration and innovation.

Yet now, as data becomes increasingly integral to so many industries, sectors and job functions, it stands to reason that HR, for example, should have the capability of running its own business queries as well as the sales and finance departments.

In the data world, there is a concerted push toward self-serve analytics. It gives more users access to business data and the relevant analytics tools, so that far higher numbers of concurrent users can collaborate on data outcomes, run their own analyses and interrogate the data in the way that suits their goals.

So, with more and more users gaining access directly to their business’ data, does it mean companies are becoming more data-collaborative? Or does self-service analytics, in fact, cause more business silos?

The importance of centralization

Today, we continue to see organizations struggling with increasing data silos and competing versions of the truth.

Self-service tools are generally more flexible than traditional BI tools. Originally, a system administrator would pre-define the scope and available data for BI reporting. Self-serve analytics, meanwhile, allow business users far more free reign to analyse the base data themselves. Most importantly, self-service analytics are intuitive and easy to use.

However, not all self-service analytics tools are created equal, and many come with limitations. If there’s a lack of central data management, for instance, companies that only offer self-service analytics run the risk of business users missing key insights, misinterpreting data or performing the wrong analysis.

Slower answers to queries

Whilst most self-service solutions are certainly user-friendly, the technology can fall down when it comes to data access. Giving users free access to the data, potentially generating misunderstandings, means newer tools sometimes fail to provide the accuracy of traditional BI. Pulling data extracts into external tools, meanwhile, can limit scalability.

To preserve data quality, self-service analytics actively encourages a higher number of users across the business to access data directly from a central lake. The issue here, however, is that the higher the number of users accessing the data, the slower the answers can be. As a result, business users are facing challenges with limited speed of running their queries.

Accuracy and scalability

By extracting data sets to their own computer, users can perform faster analysis than in the central data repository. This siloed scenario, however, can lead to recurring data iterations and modifications, and more potential data corruption.

If hundreds of users are creating their own analyses, there’s a risk of inaccurate answers. Without a central data authority guaranteeing high-level governance, business users may simply ask the wrong questions, fail to spot errors in their own data, or draw the wrong conclusions.

A balanced approach to analytics

In a data-driven world, it is increasingly important to give more business users access to data. Self-service BI removes the reliance and pressure on a central data team, and empowers more data collaboration across the business.

At the same time, this should be balanced with the need for accurate insights and centrally coordinated data quality. The key is an approach in which self-service complements and enhances business intelligence.

The self-service analytics market is constantly developing, of course. While the heyday of pre-defined, inflexible BI may be waning, self-service tools are still evolving. Many now support governance, scalability and complex queries far better than others.

Software like Kognitio, meanwhile, can let users run hundreds of concurrent queries over big data sets, directly. Such software can make the data in your Hadoop file system or big data lake as accessible and agile as if you were running it on a subset of data on your local machine.

Find out more about facilitating an enhanced self-service analysis approach with Kognitio, getting faster answers directly from your data, protecting your analytics accuracy and data quality.

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