According to a report, 85% of data projects fail, and many factors contribute to it. With the widening skills gap, and the growing sophistication of analytics tools and software, many projects tend to fail. With so many companies adopting big data analytics, 85% is a massive number that cannot be ignored.
Below, we look at some of the problems – along with their possible solutions – that are common when it comes to a data project’s failure:-
Project & Data Not Aligned With Business Needs
The business structure should be framed in accordance with the needs and challenges of the specific analytics problem that the organization is looking to solve. Oftentimes, failure happens because organizations and individuals working on the data project fail to define and align the data with the business needs.
One can counter these problems by involving subject experts with strong analytical skills and background knowledge. Hiring data scientists who can help define the problem early on in the project will be helpful.
Big data and analytics are complicated and should make sense and be accessible to business users. An organization should provide tools that are simple and easy to use for its business analytics team. This team should be able to leverage these tools for analytics, visualization and data discovery. Also, care must be taken to ensure that non-technical business users are not burdened with the task of using programmer-level tools. Easy and simple operations make it better for business teams to handle work, and come up with desired results.
Lack Of Emphasis On Data Lakes
Analytics involving big data deals with massive sets of data. Hence, organizations need to give due importance to storage. Although there are many on-premise technologies and cloud systems in place to manage this, storage is not enough when it comes to dealing with distinct types of data.
For this, organizations need to give importance to data lakes. A data lake holds several sources of data and contains many data types, which makes it easy to manage massive amounts of data associated with a data project. However, a data lake must not be used to dump every type of data in it, and should be used in a more meaningful manner.
Not Prioritizing Quality
Not having good data quality and a data management system are two of the most significant factors that lead to failures in data projects. Failure to search, curate and model data properly will only result in faulty analytics. An organization has to put systems in place which enhance the accuracy of the data, and ensure that the data is up-to-date and is delivered on time.
Ignore Key Facets Of Security
Most data involved in a data project is from clients. Some of it is identifiable and personal as well. So keeping this information secure while working on the project becomes critical. It would be unacceptable if people outside the project get hold of the data. The security measures should include establishing necessary enterprise tools, using data encryption, policy enforcement, and training on the use and access of data.
Not Hiring Good Consultants When Needed
Sometimes, organizations may have to decide between building an in-house team or hire a consultant. However, this depends on the kind of budget available, and the software the organization uses. When an organization hires a consultant, it is often on a long-term basis because of the potential knowledge transfer that could happen. A consultant can help organizations define their needs, and develop a plan to meet its goal.