Common Analytics Mistakes that Enterprises Must Look Outby Kamalika Some August 25, 2020
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.
• Not closing on Contingency Planning
That’s a step which many enterprises miss. A very crucial step that begins with devising a plan B if plan A fails. Devising a contingency plan often goes undetected because it takes a mix of all the critical resources like marketing and tech skills which do ferret them out. Knowledge transfer with the marketing teams, and development teams without understanding how tracking works in confusion on how to understand what does data and model answers’ mean causes a fix.
To tackle these common analytics mistakes, enterprises should frequently check their data accuracy and look for unusual signs offered by the analytics solutions. They must take extra efforts to decrypt the technical aspects of data tracking, to better sense problems, and raise smart questions for a seamless digital adaption journey.