Self-Service Analytics and Our Data-Driven Future

Image credit: iStockphoto/master1305

The pandemic has made analytics a primary ask for companies. It’s not just tooling up their data science team, but forward-looking leaders are looking to empower their frontline employees as well.

So is the future for self-service analytics bright? Maybe, but it does face some significant hurdles. Joseph Antelmi, Gartner’s research director for analytics and BI, sheds light. 

Many companies are talking about self-service analytics. So what are the primary motivators? 

Antelmi: Most organizations have more data than they know what to do with and not enough analytics to drive effective decision-making for a broad set of organizational users at scale. Traditional support models for analytics that required BI developers to gather requirements, prepare data, and construct visualizations are too slow. Analytics users are looking for rapid answers to sophisticated analytics questions. Analytics users prefer to solve their problems, and the larger organization would also prefer that users solve problems themselves.

What are the hurdles that stand in the way?

Antelmi: The most daunting hurdles that stand in the way of self-service analytics success are:

  1. A lack of willingness to change across stakeholders. This is particularly problematic when organizational leaders are reluctant to change. 

  2. Poor data literacy

  3. A lack of relevant skills and staff

Note that the most significant challenges typically come from people and processes, not from technology, although technology can also be a hurdle. A lack of data and analytics governance can also doom self-service success. Data and analytics governance teams must exist to manage the rules and tools to keep the organization safe while enabling broader data access. 

Self-service analytics works if the data sets are relevant to the problem statement. So how do you ensure that you do not drown the user with irrelevant data? 

Antelmi: I find it helpful to consider the original objective. A dashboard is a visualization built to display information relevant to a particular objective on a single screen so that information can be quickly monitored and understood. 

Many organizations are taking the same approach when they organize their data, using tags, folders, and searchable catalog functions. Self-service users can quickly find the most relevant data and have enough context about that data to confirm its usefulness to the specific task at hand.

What are the future trends that will define self-service analytics of the future? 

Antelmi: We are seeing the growing prominence of augmented analytics in self-service analytics platforms. AI services are embedded into self-service analytic platforms to deliver new interfaces, insights, and efficiencies into existing analytical workflows. This can take the form of mobile self-service analytics leveraging chatbots that offer natural language question and answer capabilities. It can also take the form of automated insight generation, where users import data and the augmented analytics platform surfaces relevant insights automatically. Augmented analytics can also facilitate data preparation, for example, by making joint recommendations based on the analysis of datasets.

Winston Thomas is the editor-in-chief of CDOTrends and DigitalWorkforceTrends. He’s a singularity believer, a blockchain enthusiast, and believes we already live in a metaverse. You can reach him at [email protected].

Image credit: iStockphoto/master1305