5 Reasons Why Big Data And Analytics Projects Fail?

Posted by ANKUSH CHAUHAN on November 30th, 2019

It is an age of a controlled and rational human being, who is constantly in need of assurance and evidence in the form of lengthy analytical reports and charts to corroborate his or her decision. This creates a huge demand for analysts like ActiveWizards who are experts in handling big data and organizing it in to an understandable state. If you want to learn we provide best Data Analytics Course in Delhi.

Many companies embark on great analytics projects to coax insights from the massive data sets available to them in various forms. It can be both quantitative sales figures and qualitative customer responses; the aim is to decipher something useful. However, lack of insight or clear direction can make such projects futile. Therefore, to avoid this it is important to identify and be aware of the most common five reasons for the failure of analytics projects using big data.

1. Data before question

As it is pertinent to create a business plan before starting a business, similarly it is also necessary to set an aim or question for research before using the data. It is a misunderstanding to assume that if you look hard enough at the data something of utmost importance may reveal itself. The problem here lies in the fact that big data contains information from several perspectives and to understand each one of them a different lense must be used. The lense in this case is the question that you want to study. By identifying the question you will be able to find the actionable drivers in your data.

2. Absent stakeholder problem

You may have witnessed that an analytics project involves the interests of several stakeholders and it is basically their motivation that drives the results. Hence, the involvement of these stakeholders in the big data analytics project is important. It is the stakeholder who knows what information is required to measure change. He or she is responsible for setting a hypothesis for research. However, if the stakeholder is absent from the analytical process the hypothesis becomes weak and the insights meaningless.

3. Unrealistic Expectations

It must not be assumed that starting a big data project will automatically improve your sales or will make your advertisement strategy more compelling. The bar must not be set too high and unreasonable expectations must be avoided at all costs.

4. Disagreements

Every person involved in the project may also have different targets to achieve. Trying to do everything all at once can often lead to failure. So it is important to decide collective goals for these projects and create a better communication network amongst different stakeholders.

5. Bad data

It is a mistake to assume the data to be both valid and reliable if it is big. Even with a good hypothesis-driven structured approach analysts may fail if they do not have access to clean, relevant data. Thus, this places a huge responsibility in the hands of those who plan the methodology for the research.

Therefore, the aforementioned five reasons are important for understanding why big data and analytics projects fail. To grow further, it is important to avoid these mistakes and learn from them. TGC India provide best Data Analytics Course in Delhi.