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Five Steps to Successfully Embracing Data Analytics

October 12, 2017

Five Steps to Successfully Embracing Data Analytics

5 Minute Read

Although data analytics is still in its early stages, the industry has made significant strides over the last decade.

Richard Arthurs
Richard Arthurs, FCPA, FCMA, MBA, CFE, CIA, CRMA, QIAL
Partner, National Leader - Internal Audit

While the use of big data has been linked to the massive success of companies like Amazon and Facebook, the promise of data analytics remains somewhat out of reach for many businesses. According to New Vantage Partners Big Data Executive Survey 2017, a staggering 95 percent of companies indicated they had undertaken data analytics projects in the past five years. However, with 59 percent of companies reporting that they are not able to generate meaningful insight from their initiatives, the results have been less than stellar.

Although data analytics is still in its early stages, the industry has made significant strides over the last decade. The availability of tools to gather and analyze data, which was once cost prohibitive for smaller organizations, is now much more affordable and within reach for smaller corporations.

However, companies require more than access to software and tools to develop a successful data analytics project. An initiative that’s based on purchasing software and diving into the data without a goal in mind is doomed to fail. Here are some key steps that every business should take before embracing data analytics as a tool for making business decisions.

    1. Using company strategy to create a hypothesis: A company’s strategy and business plans are typically based on industry research and basic insights gleaned from years of experience. The power of data analytics as a tool is in its ability to confirm those assumptions and provide insight that allow companies to discover what’s working and what can be improved or changed. A data analytic initiative should begin with a hypothesis of the opportunities available or the potential risks and how the data can help the business. The goal is to embark on an analysis that can provide insight to validate the hypothesis.

    1. Defining the goals: Once the company is clear on what it hopes to understand from its data, clear goals need to be established in order to determine the right type of analysis. If the goal is to get a better picture of the state of the business at the present moment, then a descriptive analysis has the potential to bring clarity to a substantial amount of information about the company. If the goal is to understand why a particular event happened, a diagnostic analysis can provide valuable insight. This is often used to detect fraud, understand accounting errors, reveal cost inefficiencies or understand customer behaviour. Predictive analysis can help a business make plans applying an understanding past events as a way of predicting future events.

    1. Narrow the scope: Too often, businesses attempt to achieve too much with their first initiatives. They take on projects that are too complex, which result in insights that are too complicated to understand, let alone implement. It’s better to begin with simple projects that can result in quick wins.
      Generally, this means running descriptive analysis before embarking on more mature types of initiatives. For example, it’s better for a company that deals with inventory to start with developing a snapshot of what's working with their inventory control system before trying to predict ebbs and flows for the next six quarters. Along with a narrow scope, it’s critical to choose tools that easy to manage. Acquiring complicated software with too many features is a common and costly mistake for many companies.

    1. Have the right team in place: Successful initiatives typically require three skillsets to work together:
      • An individual who understands the business, from the strategy to the process and the risks and metrics involved.
      • A technical expert who understands advanced analytic systems, how to properly manage data, and to test for integrity.
      • A project champion who can manage expectations of senior leadership and act as a liaison, maintaining communication and facilitating understanding between the ground team and the executive body or board.
      Each brings a level of expertise to ensure the data is understood and used efficiently.

  1. The role of data integrity: The entire success of a data analysis initiative pivots on the integrity of the data. An organization can spend money on technology and hire the best experts, but they do not have access to the data, or the data does not have integrity and cannot be trusted, the entire initiative will fail. For companies just starting out in the field of data analytics, the best approach is to work with professionals to help navigate the successful implementation of this new tool. The business insight that company or industry data can offer has the potential to influence a business’ success for years to come, but only if the information can be understood and easily applied.

Tomorrow’s technology is shaping business today. To learn more about how MNP can help you can make data analytics work for you, contact Richard Arthurs, National Leader – Data and Information Dynamics at 587.702.5978 or [email protected].


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