Governance is a critical aspect of managing organisational data and advanced analytics capabilities, and yet many organisations believe that they find the prospect of creating and managing a governance process very challenging. Governance is a holistic and inclusive process. Different roles in the governance ecosystem require different skills. Aligning with a strategy first mindset helps gain alignment with the organisational model. Organisational culture plays a vital role in shaping the governance processes. The governance process needs to balance time and cost with the business value it creates.
A modern data governance strategy weaves itself into the business and its infrastructure. Governance is present in enterprise architecture, business processes, and it helps organisations better understand the relationships between data assets using leveraging like visualisation. Most importantly, a modern approach to data governance is an ongoing process because organisations and their data are constantly transforming.
So, the approach to data governance needs to adjust as an organisation progresses in the journey. When it comes to analytics, data governance is the best way to ensure the organisation is leveraging the right data to drive strategic and operational decisions. It’s easier said than done, especially when one considers all the data that’s flowing into a modern organisation and how you’re going to sort through it all to find the good, the bad, and the ugly. To develop an effective analytics governance framework should consider the following aspects:
Subscribe to our Newsletter
Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy
ROI & Analytics Value: A common oversight is for organisations to invest in analytics tools and data infrastructure, then prematurely decide that the investment in analytics is too expensive. Instead, it is often wise to start with a small project that addresses an interesting problem that has relevant data, with an experienced data scientist, using open source tools; this can provide a quick win and build momentum for the program. Data mining engagement take time to staff-up, procure the new analytic tools, analyse data, and create the proof of value. Leaders must be able to demonstrate patience and perseverance to champion the cause. Time, personnel, money and resources must be maintained so that the project can stay on course and produce the success that is desired. Cost to mind while thinking about any return on investment, but often the work hours saved through increased productivity, represent a large part of the benefit from an analytics engagement.
Business Strategy and Performance: Analytics project decisions are often centred on the analytics software tools that are available within the organisation, or specific analytical techniques that are well understood by the analytics team. Allowing a tool or technique to drive the analytics strategy limits the types of problems that can be solved and may not align with strategic goals for the business. Using analytics to achieve sustainable competitive advantage and generate a significant return on analytics investment begins with a well-conceived analytics strategy and roadmap for success that is aligned with, and supports, the overall business strategy.
Prioritisation of Initiatives: Choosing projects that benefit the entire organisation or ensuring modeling efforts are focused on current or emerging needs provides insight to make more strategic decisions. The analytics team should assess the unique business challenges for the organisation, match those challenges with relevant data and resources, and establish processes that grow capabilities and institutionalise analytics to ensure key decision-makers have access to actionable results.
Analytics Models: If a model is not regularly updated with fresh data, business owners and end-users can become dependent on outdated model results, increasing the likelihood of biased recommendations that are not based on objective results. Once a deployed model answers the original question, look for opportunities to add new data sets and ask deeper questions. Use this initial success to demonstrate that the benefits of data analytics merit the required investment of time, money, and expertise to solve bigger challenges with a greater potential return on investment.
Analytics Capabilities: Technology continues to improve, data is being created at an ever-increasing rate, and new analytical techniques continue to emerge. A successful data analytics program must always be strategic and deliberate about moving forward. Continue to invest in analytics tools, making model results easily accessible to decision-makers, and use analytics training to enhance skills and institutionalise a data-driven culture. The presence of an analytics culture is one of the strongest indicators of future analytics success. Building an effective analytics culture can transform an organisation.
Analytics Competencies: The ideal analytics team structure allows the entire organisation to benefit from the insight provided by the analytics solutions. Analytic competencies turn the business data into information that can be used for decision making. Data science requires curious, analytically-minded individuals. These people must be technically savvy to deal with the many IT aspects of data science, be apt with the complex statistics, and be creative problem solvers. It is rare that any one individual possesses these skills, so it is important to build an analytics team that covers the full breadth of data science domain. Changing a corporate culture is never easy. That’s why successful predictive analytics initiatives demand strong leadership from one or more “champions” who are enthusiastically committed to analytics and who command sufficient respect within the organisation to enlist the commitment of others. Having a champion with direct access to a Chief Data or Analytics Officer provides the best opportunity for success.
The critical challenges to analytics governance adoption include three aspects, which include executive buy-in, inter-departmental collaboration, and Skilled resources. The next paragraphs explain each of these in further details:
Executive Buy-In: Without strong sponsorship from the executive team, the disparate departments are left without a unifying vision and typically won’t place enough value on implementing a governance program as they are blind to the critical risks of inaccurate data.
Inter-departmental Collaboration – Lack of collaboration, communication, and understanding the value of data governance will severely impact the ability of an organisation to become analytics-driven. In fact, without proper support, one can expect strong resistance to analytics adoption. Limited Resources –More often teams are small and missing dedicated resources or are reliant on constrained resources. Mostly the governance responsibilities are being added on to the job duties of already overwhelmed team members. Simply put, too many decision-makers are under-estimating the complexity and value of analytics.