In the last two decades, companies across the world have started to increasingly rely on data analytics when making major business decisions. By using relevant tools and techniques, businesses have resolved challenges, increased productivity, enhanced profit strategies and improved the quality of their services or products. Nevertheless, there have been some failures too. Some companies are unable to cope with big data due to its overwhelming nature.
Below is a list of key steps that can aid companies big and small in successful data analytics.
- Set your goals and expectations carefully
Consider the objective of investing in a data analytics project. For instance, does the company plan to increase sales or control fraudulent activities, or maybe manage the brand better? Identify the key objective and think about how data analytics can solve the problem for you. When the goal is vague and aimless, the risk is high and the potential for wasting time and resources significant.
- Build the right team
Data analytics requires a diverse team that includes IT professionals, as well as individuals with strong domain knowledge and functional skills. For example, General Electrics (GE) data team includes experts from different business backgrounds who collaborate in the delivery of innovative solutions to the company’s finance department.
- Start small, slow and steady
Grandiose investment into an analytics project does not always guarantee its success. In fact, many well-funded data analytics projects have been unsuccessful due to unrealistic goals. To increase the likelihood of yielding the desired outcomes, the business must start with a narrow scope and commit the team to resolving that particular issue. When the problem at hand has been successfully managed, the strategy can be altered and expanded across the organization.
- Leadership is key
Many projects fail due to the lack of commitment and buy-in from key decision makers. When leaders do not believe in the data analytics initiative, they tend to divert resources to other projects. As they fail to involve stakeholders and other important executives during the decision-making process, this results in an even greater chaos. In addition, data analytics projects often necessitate changes in organizational policies and procedures, making leadership support essential to the project’s success.
In 2018 and further, big data will continue to offer a high potential for growth and expansion. However, its complexity, variety and depth are bound to increase too. Businesses must be prepared to handle the dynamic nature of data to reap its benefits.