Data Analysis Outcomes: Will Your Clients Get What They Want?

Valuable use of data analytics requires laser-focus on the goal.

With all the hype Big Data and data analysis have received, it’s unlikely that your clients or prospects haven’t heard about at least some of the benefits of data analytics. Businesses can leverage the insights from data analysis to take a proactive approach to customer service or equipment maintenance, enhance R&D efforts, enhance cybersecurity, prevent fraud and more. An important question that managed services providers (MSPs) and their clients need to answer, however, is whether implementing the solution you offer will result in the data analysis outcomes they’re looking for.

When It’s Not About the Solution

World Wide Technology, a global technology solution provider based in St. Louis, points out that data analysis may not be the best path in some use cases. The company’s blog states, “The key is not to force complex analysis under the assumption that it will always lead to higher gains.”

You need to think twice before encouraging your client to invest in data analysis in these situations:

  • Data isn’t available. It’s important to acknowledge that data analysis benefits are only within the grasp of businesses with data to analyze. Evaluate the data your client’s business applications collect and data they can access through other means, and determine if it’s adequate to lead to the data analysis outcomes they need.
  • Data quality is poor. Once you see if data is available, evaluate data quality. Is it accurate, complete, consistent and timely? You’ve heard it before – garbage in, garbage out.
  • Limited impact of data analysis outcomes. Evaluate whether the business could improve performance or gain the insights it needs without implementing a data analysis solution. If data analysis doesn’t help them move the needle any more than other solutions, as a trusted advisor, direct your client to the most cost-effective, practical choice.
  • Lack of skilled people or tech infrastructure. Data analysis isn’t a plug-and-play, add-on solution. It requires advanced technologies, expertise, and the infrastructure to support it. However, this is one hurdle you can help businesses or enterprises overcome by offering Data Analytics as a Service solutions.
  • The return on investment isn’t there. Step back and take a holistic view of the proposed project. Will the investment your client makes in data analysis provide an adequate return? The data analysis outcome they want may have value, but the solutions required to get there may be too complex or costly. Make sure your client understands the math.

Factor in Ease of Implementation

World Wide Technology also points out that a data analysis use case must be evaluated based on its ease of implementation. Therefore, in addition to the work you and your clients have done to assess data quality and the complexity of the solution, you need to discuss these considerations:

  • Buy-in: Do your client’s leadership and team support the solution?
  • Compliance: Will the client need to take extra measures to comply with data collection and privacy regulations, such as the EU’s General Data Protection Regulation (GDPR) or the California Consumer Privacy Act?
  • User-friendliness: Consider how the client will integrate the solution into current operations. Will it enhance their team’s ability to work – or slow them down?

Focus on Data Analysis Outcomes

In the IT world, it’s not uncommon to deploy some solutions with the understanding that it is something that the user can “grow” into. For example, some enterprise resource planning (ERP) features can meet the business’s needs now, and as they adapt their processes and mature digitally, they can expand their use or add new features later. Or, a company may deploy mobile devices for communication and later leverage them for data collection or locationing.

Data analytics doesn’t fall into this category. Before you advise your clients to implement a data analytics solution, you need to clearly define desired data analytics outcomes and use them as a measuring stick for the entire decision-making and implementation process.