People hear terms like big data and data analytics, and automatically think, “My company needs this,” because it sounds modern and trendy. Just as often, company projects are failing or poorly executed due to the lack of accurate implementation of data analytics. The proper management and application of data analytics can make a big difference for any company looking to make big data processing more achievable. However, if not maintained and managed well in the right setting, negative results can follow. In order to be successful with your data gathering and sharing, it’s important to understand the implications and reasons why your organization’s data analytics might be failing.
Analytics is not about data, it uses data as a tool to accomplish stated goals. Data is used to make predictions about the future and support decision making concerning strategies. Business units need to be involved with forming goals for data analytics, so there is a purpose. Analytics should look at the big picture and overarching goals of a firm, not try to solve a handful of problems. Once goals are developed, find specific data trends and execute analysis that can help you make predictions and understand areas where change is needed.
One reason many data analytics projects fail is simply because of “bad data”. Databases must be vigorously maintained to ensure accuracy and best practices, along with data itself must be shared between agencies or departments. Often times, a company must reorganize entirely to make the program work, due to the necessity of ongoing improvements and collaboration. Data integration must fit the architecture of the firm, including reliable forms of data storage and information management. Often times, companies who are successful with big data analytics will implement agile methods of project management and software delivery. Agile involves forming cross functional teams, and being able to quickly adapt to changes.
Many projects fail because they are not completed on time or within their budget. Funding should come from outside of IT, so business units are invested in the success of the analytics program. The whole firm must be involved in developing goals and planning for the analytics, and must be followed up with consistent oversight. A big data program is an ongoing project that will evolve over time, not a single implementation. The data should also be in the hands of the business units, as they are the ones who will use it. The benefits of the program need to be sold, ensuring that everybody is on board with the accompanying changes. Once the data analytics are implemented, a formal training program for everybody who will be involved is essential.
Before you begin a data analytics project, understand what benefits you expect to receive from that investment, and what goals you hope to accomplish. Ensure your architecture is ready to handle big data, and make sure you’re people are on board.