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August 3, 2020updated 04 Aug 2020 11:11am

Why synthesis is key to improving analytics in a changing world

By GlobalData Technology

The recent QlikWorld Online conference came and went with no new public roadmap, but it did offer an intriguing vision of synthesis, an analytics trend that’s built on a handful of new requirements — which altogether stand as a row of streetlights for the analytics industry to see a new road.

In “2020 Trends: Analytics Alone is No Longer Enough,” Qlik senior director of global market intelligence Dan Sommer argued that today analytics needs its other half, synthesis. This seems especially relevant and timely for the new pandemic-ravaged world.

Analysis helps understand one piece of an operation at a time. It’s useful for navigating a known environment, such as one department or one function, or even whole parts of the pre-Covid world. But analysis tends to falter in an uncharted or fast-changing reality.

Synthesis is macro smart. It is at its best in an unknown, even chaotic world. It reveals relationships and commonality among disparate parts to show how they relate and form a new whole. In turn, that can launch techniques like scenario planning, in which multiple futures are imagined and planned for.

Clearly, synthesis has a few requirements that analysis doesn’t. While analysis can look closely at one operation, synthesis can examine several at once.

  • Synthesis benefits from real-time data to tie pieces together. Analysis can help improve a department’s efficiency, for example, while synthesis can help that department work among other departments and the organization’s environment.

Synthesis benefits from widespread use of data much more than analysis does

Several trends will help improve accessibility

  • Metadata catalogs, made possible by machine learning, help make all disparate data sets accessible across an enterprise and the organization’s environment.
  • DataOps with self-service analytics speeds the data pipeline as it manages the data. It automates data testing and deployment in real time, and it at least partly eliminates self-service data preparation.
  • Using AI and machine learning to make data instantly identified, evaluated for quality, made available, viewed, analyzed, and discussed.
  • Data intelligence has to infuse the organization. That requires enterprise-wide data literacy. Literacy is the ability to read, work with, analyze, and argue with data. The now legendary standard of about 35% literacy just isn’t adequate. However, organizations have found themselves unable to fix the problem, so they need professional help.

Trust is a big factor

  • Ethics and responsible computing. If data analysis depends on a plentiful and constant supply of data from a wide variety of sources, no data user can risk disrupting that flow by violating the confidence of suppliers. Ethics and social responsibility is required to ensure their enduring trust.

Of course, all these conditions map to Qlik’s capabilities, either current, emerging, or expected. Other vendors are onto these, too. It’s up to forward looking leadership to make sure the organization pays attention and actually builds in each of these conditions for the analysis-synthesis duo to do their work.