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More Data Vs. Good Data: Where Is Your Enterprise Heading? While having more data can help to paint a more accurate picture, there does come the point in which even infinite amounts of data do not improve your data's accuracy

By Sayantan Dasgupta

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It is no secret that data has become the preferred currency of enterprises. However, an avenue that has yet to be explored is whether it is more beneficial to collect more data or better use high-quality data. While having more data can help to paint a more accurate picture, there does come the point in which even infinite amounts of data do not improve your data's accuracy.

As data collection continues to grow at a rapid rate, nearly 1.7 megabytes every second, learning how to create the most advantageous collection and the use of it is essential to benefit your business. Most companies understand the importance of collecting data, but using it effectively is what matters the most.

Don't get lost in IoT

Data provides an immeasurable number of insights, but that does not mean that all data is useful. This is especially true when using data to make decisions or as KPIs for specific goals. IoT connections are expected to reach over 83 billion by 2024, so there is no foreseeable shortage of data points in the future.

With so much available data, it can be easy to fall into the trap of assuming that every data point is conducive to your company's mission and goals. Still, too much data can paint the wrong picture or simply waste your time, effort, and resources. Falling into a rabbit hole of trying to collect and analyze all of the data will result in any executive left feeling confused and overwhelmed. While IoT allows enterprises to operate more efficiently, improving customer experience, lowering costs, and exploring new streams of revenue is essential to analyze and curate large volumes of data appropriately – both structured and unstructured.

Is more data helpful?

Data provides insightful points of information that we can use to help achieve goals, create new strategies, and for many other purposes. However, is there ever such a thing as too much data? Absolutely. Data does not organize or aggregate itself; it requires software, professionals, and leadership to determine what the data means to their company and how to use it. If you are collecting more good data, then it can certainly be helpful. However, if you continue to collect larger amounts of bad data, it can cost you time, resources, and money.

If you use smaller data sets, manual analysis is completely acceptable and usually has incredibly beneficial results. On the other hand, if you are using massive data sets, your company will have to employ machine learning to create smaller sets of data that are easier to manage and understand. It is crucial to determine whether larger, more aggressive data collection is worth the associated costs. If collecting and analyzing more data benefits your enterprise's bottom line or long-term strategy, it may be worth the investment. This is a decision that is best made by many departments within an enterprise as it can affect marketing, sales, IT, financials, and strategic planning.

Good data Vs. bad data

Regardless of whether you are planning to collect and work with larger data sets, your enterprise needs to ensure that it is focused on collecting, analyzing, and aggregating good data. So, what is the difference between good and bad data? It is actually relatively straightforward. Good data is what you collect when employing a good data strategy. Good data ensures that you collect clean, insightful, and helpful data that is used to improve decision-making and create more informed strategic moves.

Bad data is not bad for any other reason than it is just not useful. Bad data is what is collected and used without a good data strategy. This occurs when enterprises allow the data to control their strategies and decisions without understanding its impact. In the case of more vs. good data, more is only better when the data is good.

Copious amounts of bad data will result in:

  • A data pool that is essentially useless without any real understanding or use for it
  • Unclear outcomes
  • Resources and funds wasted on machine learning that is not integrated with your enterprise's vision
  • Useless reporting with unclear goals and missions

Does your enterprise need more data or better data?

If your enterprise is taking an approach in which your teams are prepared to start collecting more data, then it is crucial to ensure that you are collecting the right data – good data. Your enterprise does not need more data if it does not serve your strategy, as well as your short and long-term goals. While large data sets can assist your enterprise in recognizing best practice patterns, smaller sets allow you to dig deeper into existing issues and opportunities.

Moving in a direction towards collecting and analyzing more data is not the right choice for every enterprise. The key point in deciding whether your enterprise should focus on more data, good data, or both is completely reliant on the individual goals, capabilities, and budget of every enterprise.

Adopting the right strategies

To successfully execute data collection and execution, enterprises should adhere to a few basic strategies to ensure they make the most of their data efforts. These strategies include:

  • Faster is best. The quicker your enterprise can accumulate data, the faster it will be able to analyze it. Fast data processes ensure a speedy and actionable reaction. Your enterprise will be able to streamline and increase the speed of identifying potential problems, making decisions, and countless other business functions.
  • Ensure insights are presentable. Businesses need to be sure that their data is able to communicate key insights and takeaways quickly and effectively. Using artificial intelligence and machine learning ensures a more accurate visualization of the data by continuously updating data sets.
  • Lower your costs. By preparing data and increasing technological capacity, enterprises can lower costs and increase productivity. With the right technology, businesses can create and collect data, analyze it, and separate what is useful from what is bad data.

The bottom line

Data is an incredible and necessary resource for any business, but like any other tool, it must be used wisely. Without the proper capabilities and purpose, too much data can cause additional issues. Understanding how to employ the right amount of good data ensures that your business can execute its strategy effectively and successfully. In the battle of more data vs. good data, the winner is the approach that benefits your enterprise the most. Where is your organization in this data maturity curve?

Sayantan Dasgupta

Director, Gramener

Sayantan is a Director at Gramener, a data science and analytics company that helps organizations identify insights and convert them into compelling stories. With over 14+ years of experience across B2B SaaS-based companies, Venture Capital, Higher Ed, and the Music Industry, Sayantan helps teams with their GTM strategies, Product-market fit, and Demand Generation activities. In his previous stint, he fulfilled two titles; Vice President of Marketing, North America for a US-based ERP/CRM/SIS/AI company, and Partner at a Chicago-based Venture Capital firm focused on tech investments.

Being the great-grandson of Bipin Chandra Pal, who was one of the freedom fighters of India and the 'Father of Revolutionary Thoughts', Sayantan believes in supporting entrepreneurial ideas that are solving real problems in the society. Beyond his corporate life, Sayantan is a professional guitarist and is best known for his work as a session guitarist for the superhit movie Rang De Basanti where he worked for A. R. Rahman back in 2005. During his leisure, he teaches music at his studio, speaks at events and summits, and loves traveling.


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