Why do so Many Analytics Projects Still Fail in Healthcare?

Why do so Many Analytics Projects Still Fail in Healthcare?

Healthcare Analytics

In the digital age, management of big data is the key challenge in healthcare. Big data combined with in-depth analysis can transform business processes, improve patient outcomes, lead to innovative solutions, and benefit the community on the whole. Gartner Research predicts that the worldwide analytics market will retain the top focus of business leaders in the coming years. Yet, more than half of the analytics projects fail according to Gartner Research.

Big data has long since been recognized as a “big deal” and according to a study, 64% of organizations in 2013 had plans to invest in big data. As per another expert estimate, 92% of organizations that invest in analytics get stuck and fail. A Pricewaterhouse Coopers (PwC) study involved a survey of 1800 business leaders in Europe and North America. These leaders were part of mid-sized companies that had 250 employees as well as enterprise-level companies that had more than 2,500 employees. The results showed only a small percentage of companies, 4% to be exact, reported success in data management and analytics. According to a recent Gartner analysis, 85% of all big data analytics strategies are failing and not delivering the anticipated transformations.

Common reasons why analytics projects fail:

There are many reasons why these projects fail and I wanted to emphasize on the top four reasons from my experience on why these projects fail –

 1. Failure to build solid business imperative: When companies fail to identify the clear need and value for analytics within the business, they cannot build a solid imperative. More often than not, analytics is seen as a technology solution rather than a business need. Even before companies begin to collect data, they need to know why they need the data and what they want to achieve with it from a business perspective.

2. Analytics in silos: Data explosion can be overwhelming to handle. Apart from the sheer volume of data, the content itself can be complex. The sources of data too are varied and come in many forms including unstructured, structured, text, sensor data, multimedia, video, and others. Analyzing them in silos will not provide a complete, holistic picture to drive actionable insights.

3. Data quality and reliability related issues: Poor quality and the reliability of available data is a common risk faced by analytic projects. According to a Gartner study, a majority of analysts felt that the quality of data was poor in terms of being complete and accurate and also points to the fact that 52% of the data users resort to third-party service providers to augment the poor quality of data.

4. Lack of skills: Many big data projects fail because of a lack of skilled manpower. Unavailability of analytics talent was named by 66% of analytics professionals as a key challenge in implementing analytics projects.

Closing the gaps:

In order to transform the way healthcare is delivered and managed, a technology platform for analytics alone is not going to be sufficient. Most studies have shown that many of the key challenges in data analytics are from human issues and not merely technical failures. To solve this problem, a combination of robust technology solution + advisory services through SMEs offered through analytics as a service model helps healthcare businesses clearly recognize their business need, enhance the quality and reliability of the data, and improve the patient outcomes through integrated data analytics. This model provides the solutions that fill the gaps in terms of dedicated experts who can provide valuable insights from both strategy and execution. Such a service that combines and integrates deep analytics with business processes helps achieve optimum patient outcomes and streamlined decision management.

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