Gartner terrified the IT world in 2012 when the firm published research that revealed that fewer than 30% of business intelligence projects meet the objectives of the business. This is a 70% fail rate!
What’s the reason for such big and costly projects not delivering expected value? There is a failure of IT and business to speak a common language, according to Gartner.
In the words of Andreas Bitterer, research vice president at Gartner, organizations often develop and deploy hindsight-oriented reports focusing on metrics that users may find interesting, but they do not represent the operational or strategic controls used to facilitate business performance.
Despite a lot of attention around advanced forms of analytics, a February 2018 Gartner survey showed that 64% of organizations considered enterprise reporting and dashboards their most business-critical applications for data and analytics. In the same manner, traditional data sources such as transactional data and logs continue to dominate, although 46 percent of organizations reported using external data for greater levels of insight.
With the adoption of cloud-based technologies, which require less system integration than previous generations of enterprise software, what is stopping organizations from effectively using data to minimize customer churn, increase cost savings, mitigating supply risks and reducing employee attrition?
Well, it’s not the underlying technology to store and process data, and it’s not a data quality issue because advanced software today automatically transforms and improves data quality with little manual intervention required.
No, the greatest challenge to organizations is the lack of analytics skills – employees who understand how to interpret and analyse the huge volumes of data executives know they should be leveraging to drive forward their businesses.
With 40% of organizations struggling to find and retain data analytics talent, buyers of analytics are entering a period of discontent, with software designed to deliver data to decision-makers being underutilised in organisations. So, what’s the solution?
Our clients are telling us that they want more than just cutting-edge technology that quickly processes large amounts of data. They want help to monetize their data, which requires making sense of and recommending action based on a deep understanding of their business and the market in which they operate.
As a result, three market forces are converging to create a paradigm shift that will cause ripples for years:
- The price of data scientists is soaring, with the average salary now $120,000 in the United States. This will only increase as organizations compete to attract top talent.
- To overcome the lack of data scientists, technology vendors are working on software that intelligently recommends action. This is often called event-driven analytics or AI-based analytics.
- Software vendors are also building data scientist teams for their customers, while professional services are developing technologies to help their clients make sense of data.
How do you respond? Continue to be excited by new technologies such as machine learning and artificial intelligence but recognize that traditional business intelligence and analytics are now mainstream in organizations.
Focus your efforts on creating business value from existing data sets, including customer, employee, supplier, spend, product and financial information. To help, we have provided a few data user guides that may assist you in your decisions.
Furthermore, don’t be shy of hiring data scientists from software vendors and professional services firms, who will provide people on an interim basis – as a service. This approach will not only allow you to create business value from your data, you’ll also better understand the skills required when you have budget (and accessible data) to hire a full time analytical pro.