In the past 10 years we have witnessed an exponential growth in data being created and familiarized ourselves with new concepts such as Artificial Intelligence and Internet of Things. This growth is not just noticeable in the volume of data, but also in data quality and diversity. Growth will inevitably continue to increase, and according to a study by International Data Corporation, by 2025 it is estimated that 463 exabytes (1 EB = 1 million terabytes) of data will be created daily.
In 2020, using this network of available data by performing analytics on it has become one of the biggest trends amongst companies trying to keep up with the data revolution. Analytics can be performed for multiple different tasks such as improving customer engagement and making a personalized experience, making faster and more efficient business decisions, helping finding new business opportunities, or just keeping up with competitors in the industry who are doing the same.
Considering the vastness of data which is available, and the various analytics tools and practices being used in different models by different companies, it can be hard to decide on which ones are right to adopt for your organization. This is an important step and can be the deciding factor on whether the vast amount of money your organization invests in analytics gives you the competitive edge or not. Further we will discuss some things you can keep check to make sure you are on the right track with respect to your analytics journey.
Introduce Data Governance Methods
Data Governance is a set of practices that principles and practices that ensure high quality throughout the complete lifecycle of your data. This involves the creation of a framework that fits the organizations’ pre-defined goals and fulfills the data standards to fit into the business model and further enhance the desired outcomes. Primarily governance is done to define where and in what form data is stored (exploring options such as data lakes or data warehouses), how it is protected and backed up, and who has access to it. It can also involve checking on the consistency and quality of new data. Governance gives the organization a broader consistent view of all entities that fall within the framework, and also helps with data integration.
Involve the Stakeholders
The most common misconception about analytics projects is that they should be run by the IT department, and a certain budget and time is given by the company to do these projects. The problem is that the analytics projects should have heavy involvement from the business and marketing team, as the objective is usually to predict future strategies and support decision making. If the stakeholders are not clear about what they want, then an analyst may not truly understand the real business problem and succumbs to trial and error, that often turns out to be the reason for failure. All parties affected by the project need to be involved in the entire process and communicate with each other for the right results.
A mistake made by organizations is that they devote all their resources on implementing the first version of the solution, and they lack the vision to scale the project to truly be successful or to accommodate changing requirements. Even the best projects need constant tweaking and continuous improvement to live up to the customer expectations and produce value. Explicitly, set aside a budget, resources, and expectations for rapid revisions. Let there be a common understanding within the team that changes, and sometimes even major ones, are necessary for improvement. Also keep in mind that once you start on an analytics program you shouldn’t stop. Every year there should be investment to further advance and improve the existing data strategy.
Reality Check: Goals, Budget, Expectations
It is important to know that every project is unique and provides different challenges. Most data analytics projects fail because the investment in time and budget is underestimated by a huge amount. It is necessary to have certain expectations and a solid plan before the beginning of the project, but also the mindset to deal with problems as they come up while the project is ongoing. Sometimes the analyst sets goals that may seem easily achievable before the beginning of the project, but as it goes on reaching this goal might get harder. It is advisable to perform a reality check:
- Set the bar low because it is always nice when you overachieve, but the project is considered a failure if you don’t reach your expected goal.
- Invest in understanding the functionality and business expectations sought from the project.
- Keep a realistic schedule while considering all factors, as well as a reserve budget in case it is required at a later stage in the project.
We started with the importance of data analytics and followed up with ways you can make sure your projects are being carried out the right way. Incorporating these ideas will introduce a shift in the entire business paradigm, and help your company become a data-driven organization. This will require trust and cooperation from the executives and, if carried out properly, you will see results where your smart decisions are helping the organization increase efficiency and productivity, as well as reduced turnaround time.