Data Processing for Data Analytics is Like Eating an Elephant

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Data Processing for Data Analytics

The day when data analytics, clubbed with data processing, was discovered; it proved its worth for organizations to attain results needed for making informed decisions. It has now been a universally accepted fact that data is the life line for any organization, as it plays a pivotal role for organizations to thrive and survive in today’s data driven culture.

Data may come from any source, but merely having the data is not enough. It is what you do with that data, and the insights that you gain from the data, is more important. This makes it more than necessary for one and all to have a sound strategy for data management and analytics.

The results of a survey conducted on 376 individuals responsible for data initiatives, of which 104 in the executive capacity, is really eye catchy.

  • All of them agreed to the fact that data analytics is really important.
  • 48% executives said that data analytics was critically important.
  • 88% of the surveyed confessed; that they failed in their recent data initiatives.
  • Data inflexibility is one of the biggest challenges faced by both, data and analytics.
  • 59% of the executives found their analytics structure to be inflexible.
  • 75% of the executives were not able to act on their business requests due to the inflexibility of analytics.

So does this mean that because it is a tedious and skillful task, organizations should leave it as is, and continue doing what they have been doing? The answer is no. Data analytics backed up with data processing, and followed with artificial intelligence is like eating an elephant. And while eating an elephant there are two things that need to be considered religiously; we cannot have all of it in one bite, and where ever we start eating it from – we will have to eat it for a long time.

  • Cisco, Intel and General electric are consistently improving the cost and quality of their manufacturing.
  • IBM, BorgWarner and BMW have mitigated their supply chain risks.
  • Trenitalia, KoneCranes and Honeywell in preventive asset maintenance.
  • Last but not the least; Facebook, Google and Amazon are showcasing ads to each of us when we browse their content.

These dignitaries are taking small bites of the elephant, and it certainly tastes good. It is crucial yet simple to understand that data analytic experts assisted with data processing service providers; work towards making the mysterious data mathematics enjoyable for you, and help your organizations to succeed.

Make data processing and data analytics work for you and not the other way round

If your data does not have a purpose, your organizing is merely spinning the wheels; ready to act upon unexpected outcomes – without surety of results.

However; it is high time it is accepted that data analytics backed with data processing has the potential to transform how companies organize, execute, operate, manage talent, generate revenue and create value. As mentioned above in this write up, some of the leading companies have realized and opted for it – and are of course reaping the benefits – but not up to the mark. Reason being, the C-suites who are empowered to drive gigantic business changes that are required to leverage data processing and analytics, are in a muddle whether to enter this game of mysterious mathematics or not.

This at times is understandable. It requires complete knowledge of the methodologies to comprehend huge data sets. They should be leaving it to those who are experts at data processing and data analytics. But this also means that they should not be leaving it completely, as data analytics is a typical business matter. The team of C-suite should be able to clearly articulate the purpose of the activity and then able to implement the actions across the organization, wherever the insights from analytics will be put at work.

Data governance in organizations

Today every single organization is focused on making their applications smarter, but no or a very few of the companies, have their data governance policies in place. The situation worsens when these organizations start comparing their poorly managed prospect and customer data with incompetent data sets which lack the quality and breadth – and miss out on accurately determining where the gaps exist in their own database.

Every sales or operations team is struggling today with the quality of data that is been given to them for achieving their targets. This issue of quality can be resolved in two ways. Either, hire data processing and analytic experts to remove duplicates, clean up data, refresh common elements & things like that. Or ignore the problem and focus on the new incoming data that is stacking up leaps and bounds – every second. The latter option could turn out to be disastrous and is certainly suicidal for organization of any size.

Conclusion

Fundamental data governance should be resolved at the earliest. Without these data in place, it becomes nearly impossible to predict outcomes or even make informed decisions if the underlying data is stale, inaccurate or incomplete. It would not be wrong to quote the example of education in schools; if one does not have strong foundational curriculum in school, the individual ends up making uninformed and unreasonable decisions when they enter the working world.

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