Analytics is an integral part of a successful project. However, the number of tools and working methods sometimes may lead to a kind of bewilderment. The statistics show that only 25% of data analysis models are good for implementation. Professional analytics consulting services will cope with all tasks effectively, but to decide on the most suitable analysts you must understand basic mistakes and challenges.

Due to this our team has made up an inventory of the most widely spread pitfalls of data analytics projects. So, make yourself comfortable and so not forget to make some notes.

1. Lack of data and disinformation

Nowadays it is a difficult issue to draw the line between the truth and a lie. Because of multiple interpretations of the facts, analysts’ work becomes a real challenge. To ease the process of data assessment it is pivotal to choose the best working environment. Such tools as Data Lake and Data Warehouse may be entirely beneficial.

2. Understanding the real issue

To make maximum use of data analytics a business owner must understand the scope of his or her interests. It is essential to establish close ties with the chosen experts. Poor communication and misunderstanding between business owners and data scientists can even lead to bankruptcy.

So, before hiring an analytical team make sure you are surfing the same wave and have common objectives.

3. Information science model problems

The key reason for data misinterpretation is lack of education, expertise and training. Moreover, insufficient practical skills may be another problem. High-quality data analytics requires vast experience, because analytical work presupposes a number of stages and tools. Since our world changes extremely fast, experts must keep abreast of the times and trace all modern tendencies.

4. Solving not popular problems

It is of pivotal importance to establish the scope of your interests. Well, analysts can analyze all available information, but it takes time and money and energy. It will be better to choose particular vectors of work and focus on them. Otherwise, you stand a risk of missing the most important aspects of your companyโ€™s operation. If you find it difficult to determine your business vulnerabilities, apply for professional help.

5. Unrealistic expectations

Some people mistakenly hope that data analytics can solve all the problems. That is why a lot of business owners hire professional analysts only when a problem arises. However, it is a bad strategy. Analysts should help you from the early stages of establishing your company.

Moreover, you must be realistic and sceptical. Try to find small issues and focus on them. Do not strive to cope with all the aspects of work simultaneously. Otherwise, it may turn out to be a real disaster.

6. Poor management

All successful projects need serious preparation. Business owners must understand that data science also includes code writing, table composition, managing vast data flows, etc. Due to this timing, quality, scope and budget are essential for organizing high-quality information analysis.

7. Method failures

There are a lot of different working methods for conducting analytics. However, if you donโ€™t have a strategy and just try doing things intuitively, then you are just wasting your invaluable time. Before choosing the analytics tools, read all available information about them and only then make decisions. Nowadays among the most popular analytics instruments are: SolveXia, SAS, Tableau, Python and many others.

8. Lack of rationality

Last but not least, use analytics only with good purposes! We must remember that all people are equal and must be treated in the same way. Firstly, donโ€™t take pressure on data scientists and IT people. Analytics is not such an easy thing as you may think.

Secondly, use the acquired information to make your business thrive, not to harm your rivals. This issue is of pivotal importance! Healthy communication and competition must be our top priorities!

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