More than Insight: Extracting Business Value from Data Analytics

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Real-time data analytics, insights, and observability will shape the future of business

In a previous blog post, Analytics is the New Green, we noted that roughly 65% of global enterprises planned to increase their analytics spending that year. Now, this year, many CIOs have placed data analytics and warehousing as the third most important area for investment for the coming year1, with nearly half planning to add ten or more data sources over the next 18 months.2 In the next three to four years, almost 87% of CXOs claim that becoming an “intelligent organization” is a top business priority.3

There are many reasons behind organizations planning to invest heavily in data. Most of these are rooted in money—making it or saving it. Two examples are using data-empowered decision-making to create a competitive advantage or being first to market with a new solution. Much can be gained when the math—the analysis—is performed.

One example, as described in the Global Top 100 Innovators in Data and Analytics report by Aleksandar Lazarevic, VP of Advanced Analytics and Engineering at Stanley Black & Decker, is that his organization “generated a procurement analytics tool that leveraged commodity pricing, currency changes, labor, and other external data to predict cost fluctuations and make procurement recommendations which helped planners to negotiate rates with suppliers better, saving the organization close to $100M in both direct and indirect spend over the last two years.”4

However, the real power of data in business comes with its connection to everything else surrounding it.

Speed and scale with advanced technologies

Big data is often fueled by more data from more sources, such as the internet of things (IoT), industrial IoT (IIoT), and industrial control systems (ICS). Data analytics is also taking place much closer to where it is needed, over 5G channels and at the edge directly on the end-user devices. Combining low-latency delivery with high-speed analytics makes it easier for organizations to create new, always-on mobile applications fed by new IoT sensors and powered by edge computing.5

Machine learning (ML) and artificial intelligence (AI) have also become a bit democratized alongside digital transformations rooted in data and cloud computing. So much so that more than three-quarters of strategic cloud conversations focus on analytics, AI, and ML.6

Value comes from a more extensive “observability” model

Organizations need to move beyond raw data collection and analytics to reach real data-driven business value. Making the most of the data available to the business means taking any insights gained and scaling the data operations processes while keeping humans in the mix to achieve observability. While the team will almost certainly comprise data scientists, the developed systems, algorithms, and displays shouldn’t require data expertise to gain value from these efforts, as there is a significant gap in specialty big data and analytics skills.7

According to a Gartner study, nearly three-quarters of organizations that successfully applied observability by 2026 will achieve shorter latency for decision-making.8

Having this observability will increase the speed of decision-making and address the lack of understanding of the business context and user needs, which happens to be the number one cause of failure in data-driven projects.9

Real-time access to quality data is key to success

The reality is that less than a quarter of companies have access to real-time data today10, which can account for the failures of many data-driven projects11. Legacy infrastructure and data silos are common contributors to access issues.12

Data access alone isn’t enough. The data needs to be good, and its quality and integrity needs to be maintained throughout its useful lifetime. Data quality is a top bottleneck for 55% of organizations.13

It is estimated that 82% of companies are making decisions based on stale information. As many as 85% of these companies state that they are making incorrect decisions based on this stale data, leading them to lost revenue.14

How will the future shape up?

Data analytics will likely continue to progress on many of these trends:

  • The use of advanced technologies, including artificial intelligence and machine learning
  • Investments in observability to democratize data-driven decision-making throughout the organization
  • Dedicated shared services teams and strategic partnerships for data-as-a-service in the form of DataOps and perhaps even FinOps as more analysis is performed in the cloud

Finally, likely, these trends surrounding environmental, social, and corporate governance (ESG) will push data analytics to further heights as organizations not only try to achieve optimal revenue but to do so in a way that meets market expectations for sustainability.

  1. 1 – siliconAngle/ETF, CIOs are in a holding pattern – but ready to strike at data monetization, Accessed January 11, 2023
  2. 2, 10, 14 – FiveTran, Lack of Real-Time ERP Data Leads to Business Risk, Poor Decisions, and Lost Revenue, May 2022
  3. 3, 6, 12, 13 – IDC, Optimise Your Business Operations with Insights: Value of Automated Data Pipelines and Cloud Analytics, October 2022
  4. 4 – Corinium, 2022 Global Top 100 Innovators in Data & Analytics, January 2022
  5. 5, 7 – Accelerance, 2023 Global Software Outsourcing Trends and Rates Guide, September 2022
  6. 8 – Gartner, Top Strategic Technology Trends 2023, October 2022
  7. 9, 11 – 54th Hawaii International Conference on System Sciences, Beyond the Hype: Why Do Data-Driven Projects Fail?, December 2020