Top 5 Reasons Why Your Data Analytics Project Isn’t Meeting Your Expectations

October 13, 2017   //   Business Intelligence

While nearly every business today recognizes the immense potential of leveraging big data to improve decision making, the majority (60%) of big data projects fail to go beyond piloting and experimentation, according to Gartner. Why is that?

Like all other technology-driven initiatives, tapping into the potential of big data begins with shaping a plan. Identifying requirements, resources, stakeholders, dependencies, budgets, and timelines from the very beginning is critical to ensuring a successful outcome. However, what is often overlooked are a series of unique challenges that need to be clearly understood or addressed before a project can even begin.

In part one of this two-part series, we’ll build awareness around the top challenges facing business and IT leaders when building a strategy around data analytics. We’ll then provide ways to overcome these challenges in part 2 of the series.

Top Data Analytics Project Delivery Challenges

Despite the large investment in data and analytics solutions, the simple truth is that most projects and programs fail to meet expectations. In my experience managing countless data analytics projects, here are the top five reasons why.

  1. Setting Realistic Goals:
    Early in a data analytics project, articulating the end-product is a challenge. Requirements documentation, report mockups, source to target mapping, and data dictionaries are absolutely instrumental. But there is an inherent difficulty in defining abstract data needs, and having those match all business users’ practical expectations. All too often, I’ve seen business and IT leaders develop their own priorities and silos, which is a large reason why so many big data projects fail.
  2. Lack of Resources:
    The power of data science has seeped into businesses of all shapes and sizes, and the opportunities are astounding. Data analytics is arguably the most valuable tool for business leaders, however most midsize organizations don’t have the expertise on staff to build scalable solutions and ecosystems. And even if they could afford to hire someone, attracting and retaining top talent is nearly impossible when larger technology firms can offer better salaries and career opportunities for one of the industry’s most in-demand skill set.
  3. Project Champion:
    The implementation of a big data platform requires skillsets across the organization, including the adoption of new technologies, configuration, load/performance testing, and risk management. Sponsors of big data projects may not always have the best insights into data analytics project health, nor are they given adequate checkpoints to make impactful decisions based on unbiased truths in project delivery. After deployment, project sponsors tend to disband and departments are expected to continue the momentum, but often lack the training or context to evolve the solution.
  4. User Adoption:
    The technology behind a data analytics solution has no inherent value – it is the people that make it a success. After all the time, energy, and money are invested in a project, the true measure of success is how well the end-user is leveraging the analytical insights. Often times, employees are too busy, unengaged, uninformed, or unenthusiastic about using the end product, grounding a data analytics project before it even has a chance to take off. A critical, but often missed, step is ensuring you maximize user adoption while anticipating and preparing for disruption to your routine business operations and cultural habits.
  5. Rapid Pace of Innovation:
    We live in a world with continual growth in technology and an explosion of data. As innovative businesses adapt their processes to scale, analytics solutions must evolve accordingly. There will be more data created in 2017 than the previous 5,000 years of humanity. As businesses race to capitalize on this digital gold rush, software providers are simultaneously speeding up their releases cycles with a new, more flexible, easier-to-use applications or platforms. So, how do you balance technological innovation while ensuring proper governance and data quality?

While most business leaders recognize the incredible value of data analytics, many still feel overwhelmed by the prospect of transforming data into actionable insights. However, it is entirely possible to incorporate advanced analytics into your business operations with a cost-effective, manageable, controlled approach.

In part two of this series, we’ll explore the ways in which mid-market organizations are building a culture of data-driven decision-making within their organizations with the potential to solve complex business challenges and drive future growth.

Midmarket Guide to Data Analytics