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Stay Away from these Mistakes with Your Data Featured

Stay Away from these Mistakes with Your Data Priscilla Du Preez

Big data and analytics have become a success story for some organizations. However, most of them have never realized the full potential of big data because the real power of this technology has been restricted to small parts of their operations and businesses. Therefore, while others have immensely benefitted from big data, some are still struggling to see how it will help them. According to Gartner, only 20% of analytics insight will deliver the business outcomes this year. This low percentage of delivery is due to various mistakes. Due to this challenge, this article lists some mistakes you should avoid in your big data initiatives.

  1. Failing to identify the right business use cases

Failing to identify the correct business use cases and establishing clear criteria for success and KPIs is one of the failures that most organizations make; thus, their big data initiatives and implementation fail. For an organization to effectively capture feasible use cases, they need to put in place various measures, including working with different businesses and leaders and establishing clear success criteria. Furthermore, after brainstorming, companies need to develop a list of over 30 companies to use as use cases. They should then select the most relevant use case based on viability, the complexity of analytics and potential return on investment, among other aspects.

  1. Focusing on short-term gains

While short-term gains are good, they are not enough for current times. The information age we live in means that every decision, regardless of the industry in which an organization operates, must be backed by relevant and authentic data. This means that the era of personal intuition and personal experience being relied upon to make decisions is in the past. Organizations now realize that the data collected from customers has immense value. Information is an asset that must be properly managed using the available technologies, which simplifies the collection of data, auto-scaling and using technologies like AI. However, most companies tend to focus more on short-term value from the available tools and ignore everything they would achieve in the long run.

  1. Failing to focus on visualization

Visualization is a game-changer in management, and data scientists should dedicate most of their time to understanding the data through various visualization approaches. Sadly, this is not always the case because data scientists sometimes fail to dedicate much time to understanding data through visualization. In the era of heated competition and reliance on data for decision-making, visualizations are crucial in identifying patterns and making the right decisions. As such, companies must hire data scientists who know the goals of visualization and their basic principles.

  1. Lacking a central authority

Regardless of the size of an organization, be it big or small, accuracy and quality of data is a key issue that is always recurring. Data is always ridden with inconsistencies and duplications, which requires an effort to clean. One way of maintaining the standards in data collection is to have a central authority to carry out oversight. This will ensure duplications, incorrect data usage, and bad input are avoided altogether. As a leader, establish a committee or give the duty of keeping data clean and training staff that will use the data. Since data is a crucial tool for any organization, leaders should give it utmost attention and never shy away from investing in it to bring the desired change.

Generally, although there is no doubt about the benefits of big data, many organizations have not achieved it. Companies must therefore assess how big data initiatives may fail and approach the implementation with robust strategies that will help mitigate the known risks.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

scottkoegler.me/

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