Busting the myths: data analytics is not a black art

Busting the myths: Data analytics is no black art

Busting the myths: Data analytics is no black art
Author : Riya Savjani   Posted :

Even when data analytics has got people in every industry talking, a lot of people still consider analytics and big data as something that they can ignore – when in reality, they are about to be run over by the steamroller that is data analytics.
The volume of data is exploding; more data has been created in the past two years than in the entire previous history of the human race.
Many of the early adopters of big data and analytics have faced a lot of challenges in setting a right implementation plan in place, and unfortunately, have experienced a poor return on investment. Since then, we have witnessed loads of improvement in this area and have overcome many of the shortfalls. However, what still prevails is a series of myths surrounding the discipline of data analytics that some feel still has some truth to it.
So, let’s dispel some of the analytics myths, one at a time.

Myth 1: Only biggies need advanced analytics

Most of the companies across varied industry sectors think that they are doing “pretty good” and don’t need analytics or any other advanced technology to improve their business. No matter how big or small your business is you will always need advanced analytics to resolve the complexity and get improved business insights. Organizing older data and then analyzing it for insights was the first approach that businesses adopted, however, using dated data will only put you at a competitive disadvantage. This is why it gets necessary to understand real-time data trends and make use of even today’s data to predict the behavior of tomorrow.
Takeaway: Big or small, every business deserves to be on top of their data and the insights that they can derive from them using analytics.

Myth 2: Big data and data analytics go hand-in-hand

For many, the concept of data analytics has always to do with big data. They think they need a huge pile of company data before getting started with data analytics. Simply collecting data without a plan is what increases the complexity and the costs. On top of that, flawed data can manipulate the analytics and lead to decisions that could do more harm than good for the business. The usual misconception is that more data is better, due to which companies are focusing on collecting humongous data rather than collecting specific data that really matters and affects their business decisions.
Takeaway: Enterprises don’t need huge volumes of data to start analytics; they just need relevant data that will affect their business decisions.

Myth 3: Analytics takes a lot of investment to get started

People assume that data analytics is a costly affair and can only be adopted by organizations with ultra-modern IT infrastructure or big budgets to afford such technology. In reality, not all the data analytics or big data efforts require major investments. For instance, you can rely on cloud-based big data and analytics solutions to start small and then scale to your big data dream as and when needed. A lot of companies already have the infrastructure that they need to get started with data analytics, and maybe your business is one amongst them.
Takeaway: Starting with your first advanced analytics project is a lot easier than you assume it to be.

Myth 4: You need to hire data scientists to deal with analytics

Another common myth that persists is that more data analytics need more data scientists. Data scientists – being the most in-demand job in 2017 and the media spreading the news of shortage of data scientists, made everyone in the industry think that they can move ahead with data analysis only if they have someone from this scarce breed of professionals. There are number of myths about data science, but the truth is that anyone who has a clear understanding of the business needs and knows what kind of data is critical for decision making can handle your company’s data analytics. All you need is to provide some training and handover some tools to the people who run the business to gain analytics-driven insights.
Takeaway: No need to hire data scientists to deal with your business data, instead hand it over to people who already know your business model and can make use of tools to get some valuable insights out of that data.

Myth 5: You must measure all the involved parameters

Once you start your data analytics journey, you may feel like analyzing every KPI and parameter that you possibly can to derive as much information as possible. However, this will not be an ideal approach to deal with the bulk of data that your business generates on a daily basis. You should know the exact reason of measuring a specific parameter and how will it affect your decision making, rather than analyzing all the irrelevant data. To make analytics fruitful for your enterprise, you need to lay out specific objectives and measure certain data sets that can offer you some real value.
Takeaway: You don’t need to measure everything; you just need to keep track of entities that really matter. Specific data will help you drive specific results.
After debunking all the data analytics myths, you definitely can prevent derailing of some of your next projects. Data analytics is no black art, if you understand the math.
The question isn’t whether data analytics is here to stay; the real question is are you ready for it?

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