Common Analytics Mistakes that Enterprises Must Look Out

Covid-19 has bought a transformational change as how organisations look at technology. Adopting to analytics is the first step towards a change and organisations are bracing themselves. With data going digital in this pandemic read the most common analytics mistakes that enterprises must look out. Latest Developments in digital technology may prove challenging for analytics and data management.

Latest Developments in digital technology may prove challenging for analytics and data management.

The massive expansion of data sources has led to new developments in the technology paradigm. Analytics becoming the next big buzzword in the industry cannot be ruled out. Even implementing an effective digital transformation strategy can be challenging, without a substantial analytics plan.

To win over the challenges, that analytics may bring we decrypt the common analytics mistakes that enterprises must look out at their digital transformation journey-

• Starting without a Blueprint

Every organization is different and so are its analytics requirements. With the ongoing Covid-19 pandemic that has changed it all, technology adoption is something that organizations cannot avoid. Jumping into the analytics bandwagon just because every other company is doing so does not solve any purpose. Thus, an enterprise must begin with a concrete blueprint and its plan of action to implement Analytics for Digital Transformation.

• Not selecting the most Apt Tech Tools

There are multiple tools for solution implementation. For instance, to implement RPA 10+ vendors are eying their share in the user market. Selecting the best vendor/ tool with its different offerings is itself a challenge that enterprises must be aware of. Projects may fail if the correct tool is not adapted leading to operational cost escalations.

• Losing out on Data Sources

Multivariate data sources mean multiple data interactions. Data management can be challenging, through multiple sources and multiple varieties. Though it is tempting to capture every single possible data point that may cause more harm than good. This may leave the organization in quandary deciding upon the best data pipelines for analytics. It can be a waste of time and enterprise resources to chase fancy data for murky insights while the fundamental metrics are overlooked.

• Missing out on Error Tracking

Tracking errors can prove to be devastating to the enterprise. Errors lead to unreliable data and misleading analyses. Enterprises in their data transformational journey with contentious tracking issues can land in potential hara-kiri. Many things can go wrong, for instance, developer mistakenly transferring incorrect values, or selecting the wrong tool or building the wrong data model etc.

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