10 Megatrends in Analytics 

Key lessons companies should learn from analytics trends.

As the digital world becomes more complex, leadership teams are becoming increasingly dependent on analytics to help guide actions and decisions.

“Realistically, if we want to improve, we have to look to our analytics team,” said Gareth Herschel, research director, at Gartner Symposium/ITxpo in Barcelona, Spain. “Analytics is the glue to help achieve objectives given the culture and the strategy that a business has.”

Analytics can be complicated, but ten megatrends will help guide companies.

analytics trends

  1. Data to decisions
    Enterprises need to shift from data-centric to decision/change-centric. Consider the question: Why are we doing this analysis and what are we actually trying to do. Begin by looking for areas in the business that need to be changed or are in the process of changing. Any analysis will be used to improve a problem area and to influence and guide the change, instead of remaining an analysis that no one acts on. Everything starts with the change and what decisions are associated with that change.
  2. Tactical to strategic decision makers
    Look at who is consuming the information within the organization. Analysis is valuable to everyone in the organization from the CEO down and should impact strategic decisions. To influence strategic decision-makers to make better decisions, focus them on three analysis- based areas: A balanced mix of strategic and financial targets, realistic self-assessment, and an objective review of business cases.
  3. Core functions to ubiquitous
    Analytics must span the enterprise. “Every process, every function every individual becomes a consumer and user of analysis,” said Mr. Herschel .
  4. Aggregate to detail levels of data
    Analytic detail can allow more tailored strategies. Finer levels of granularity will allow better understanding of customers and challenge assumptions. This won’t determine a strategy, but rather educates decision makers as to what the strategy could be.
  5. Data silos to multiple dimensions
    Breaking down silos will offer new perspectives and will allow the team to combine multiple viewpoints to better understand what’s going on, why it might be happening and what action to take based on the analysis.

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  6. Reporting to discovery
    Organizations must make a fundamental shift in thinking to better engage with the data.and be as curious about what happened in the past as they are about the future. It will allow you to understand the environment and the potential. Engage with the data to gain new insight.
  7. Human to AI
    Have an elevator pitch about the power of analysis and what it can do for the business. It doesn’t matter what technique was used because they’re going to do the same things with varying levels of sophistication. Artificial Intelligence (AI) is interesting and powerful, but isn’t necessarily doing anything radically different, it’s just doing it radically better. Understand what is possible, and then decide what level of sophistication the organization needs –  whether it is a data scientist or AI.
  8. Platform choice to analytic portfolio
    Platform choice will drive differentiation. Select a platform that will allow the enterprise to build something unique to the company that moves away from commoditized offerings. Decide whether the company needs to hire data scientists and a platform or if business analysts with packaged applications are acceptable and what role external service providers will play.
  9. Stand alone to embedded analytics
    Embed analytics into the accelerating business process. What previously took weeks in business, now takes days and what used to take one second is now measured in milliseconds. Adjust analytics and take action in an appropriate time.
  10. Data confidentiality to open data
    One streaming service decided to share data they had about ISPs. It was internal data that, when shared, generated a large amount of publicity and goodwill towards the company, plus it served as a public shaming for ISPs. The service was interdependent on the ISPs, but by publishing the data, they fundamentally altered the dynamics of the market. This forced the ISPs to improve on something they didn’t want to do by sharing the data.

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