5 common analytics mistakes that enterprise must avoid

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Large volumes of data sources have to lead to new development in the technology world. Analytics are emerging in the industry which cannot be ruled out. Implementing an effective digital transformation strategy is challenging now without an analytics plan. To cover all the challenges the common analytics mistake that an enterprise must look out for their digital transformation journey are :

Beginning with a blueprint: every enterprise must begin with a concrete blueprint and its plan to implement an analytics solution for the digital transformation. Every organization is different and hence its analytics requirements too. With the COVID 19 impact, new technology adoption is something every organization has to look at.

Look for most Apt Tech tools: enterprises must look for selecting the best vendor/tool with different offerings, since the project may fail if the correct tool is not adopted causing operational cost escalations. Since there are multiple tools for solution implementation but to implement RPA 10+ vendors are focusing their share in the user market.

Lost occurring in the data source:  data management is challenging through multiple sources and multiple varieties. It is very easy to capture every single possible data point that causes more harm than good. This makes the organization in a situation to decide upon the best data pipelines for the analytics solution. It increases huge waste of time and enterprise to go behind the data to gain insights that are not useful making the fundamental metrics overlooked.

Error tracking: error tracking has been a huge problem for enterprises. Errors lead to unnecessary data and make the wrong analysis. Enterprises in their journey to data transformation with the contentious tracking issues mostly land in a potential problem. Several things go wrong such as the developer can mistakenly transfer incorrect values or select the wrong tool.

Contingency planning: enterprises mostly miss this step, it’s a very crucial step that begins such as if plan A fails then plan B is adopted. Invent a contingency plan go undetected since it takes a mix of all the resources such as marketing and tech skills which do expend them out. The knowledge that gets transferred from the marketing team and development team without understanding how to track works in confusion and how to understand what a data and model answers mean causes a fixing.

 To solve all these common analytics mistakes the enterprises should check occasionally their data accuracy and the look for the unusual signs given by the analytics solutions. They must spend extra efforts to decrypts the technical aspects of data that have to be tracked for better sense problems and raise relevant questions for seamless adoption of the digital journey.

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