5 Steps for Improving Enterprise Intelligence

It’s understandable why many corporate executives and managers view the sea of digital data available to them with some uncertainty. Most believe data holds great business value, but many remain uncertain about how to best extract that value.

Uncertain or not, organizations must become more data-driven in order to remain competitive in the digitally oriented and accelerated world. That’s why nearly 9 of 10 (87%) of respondents to IDC’s Q1 2020 industry leaders survey say creating more intelligent enterprises is their priority for the coming five years.

That’s certainly a worthy goal, but what does it mean in practice, and how can companies best pursue that end? IDC recommends five actions that organizations can take to increase their operational and cultural intelligence.

1. Invest in technology to address each step of the Data-to-Insights pipeline

In two earlier posts, we described the stages of the Data-to-Insights pipeline (Identify Data, Gather Data, Transform Data, and Analyze Data) and the decision-making and business benefits that a powerful pipeline can deliver. Today, organizations can select from a wide variety of technologies and cloud services – many of them leveraging artificial intelligence advances – to make each of these stages faster, more efficient, and more effective.

2. Hire a team of data, analytics, IT, and business subject-matter experts

No matter how intelligent technologies become, human experts will continue to play critical roles in data selection, processing, and interpretation for the foreseeable future. In fact, hiring people with the diverse skills and experience required to collectively approach data management and exploitation in a holistic fashion is arguably the first and most critical action on the journey toward developing greater enterprise intelligence.

3. Ensure that your team of experts collaborate in creating Data-to-Insights pipelines

No single type of expert – be it data scientists or business strategists – can provide the holistic knowledge required to get maximum value from digital data. Organizations can’t even identify the best data to analyze unless they fully understand the core business processes and objectives that data can facilitate. Much like data itself, if experts work in isolated silos they can’t deliver the benefits possible from a cross-functional and collaborative team.

4. Optimize each stage of the data pipeline to boost its overall benefits

Appropriately, optimizing a data pipeline depends on data – or more precisely metrics – to identify bottlenecks and verify improvements. The truism that “you can’t improve what you don’t measure” has become a common business mantra for a good reason. Organizations need a solid, baseline understanding of their critical processes and KPIs to be sure optimization initiatives actually deliver expected improvements.

5. Design an agile pipeline that can accommodate rapidly changing data architectures and cloud technologies in order to deliver competitive differentiation.

If there’s one certainty in our digitally transformed world, it’s that we can expect continuing and rapid change. Data-to-Insights pipelines must be built with an eye toward flexibility and constant evolution. After all, data itself is ever-changing – as are business needs and objectives. If the pipeline can’t adapt to the changes occurring at both its ends, its value will rapidly deteriorate.

Qlik offers technologies and services for building and optimizing data-to-insights pipelines and institutional expertise for achieving enterprise intelligence goals. To learn more, click here.