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Common Barriers to Analytics Success

Common Barriers to Analytics Success

3 Minute Read

The is a growing number of organizations implementing analytics and big data projects. However, many still struggle with achieving their desired returns on investment.

The is a growing number of organizations implementing analytics and big data projects. However, many still struggle with achieving their desired returns on investment.

If you read the myriad vendor surveys in the marketplace, you’ll see most of the responding organizations have employed data analytics. The most common use cases include IT operations management and security – though many others are also conducting fraud analytics, big data analytics and IT governance / compliance initiatives.

These survey respondents typically identify challenges such as infrastructure and staffing requirements, barriers to scale, cost, slow analytics and technical issues. The 'need for speed' alone emerged in the results of more than 30 different surveys.

What is fast?

This brings up the important question of precisely how to define ‘fast’. Organizations who want real-time analytics require responses within milliseconds. Though most surveys indicate businesses are leaning toward ‘human real-time’ – with acceptable latency of between five seconds and five minutes.

Unfortunately, an overwhelming number of organizations identify that their current technology is incapable of even delivering on the human real-time analytics. Therefore, shifting to real-time or machine data analytics will generally require infrastructure upgrades and a limited number of critical use cases to fit within their financial capabilities.

Investing in the Future

From a staffing perspective, the organizations currently performing real-time analytics have invested in hiring data scientists and / or several senior data analysts – and skewed their salary grids in the process. These are scarce resources in Canada and come at a premium in the marketplace.

Many businesses are now attempting to hedge those costs by hiring university graduates from schools with business intelligence and analytics programs. They’re betting it will be more cost effective to develop future data scientists by aligning them with senior internal resources who can mentor them in the business of the organization.

An Iterative Approach

Firms who have successfully launched analytics programs have taken the time to address these barriers one by one, in a series of positive outcome-generating pilot initiatives. With each successful pilot, they have reinvested the cost savings and realized benefits back into the program. They have found a path to success by focusing on one trial and one barrier at a time.

Tomorrow’s technology is shaping business today. To learn more about how MNP can help you overcome your data analytics challenges, contact John Desborough, Director Consulting and Technology Solutions at [email protected].


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