Data Analytics is rapidly becoming one of the most popular buzzwords in the tech industry today, and a necessity for businesses across multiple industries for strategic decision making. It does this by using statistical methods applied to an algorithm and automated to glean insights from data. Due to its popularity, a lot of myths and misconceptions have come up about it. In this article, we will list down the most popular fallacies surrounding Data Analytics while providing clarifications for each one.
Let us begin with the thing every CEO is worried about when implementing something new to their businesses:
As with everything that can improve businesses, Data Analytics requires some degree of investment. The resources put forward to successfully implement Data Analytics need not be crippling or even substantial. We have stories coming from established businesses that use outsourced Data Analytics experts to give them a boost to get ahead of their competition. They integrated the insights gained from those partnerships into their Business Intelligence to operational solutions. They did all this without having to allocate a single cent to an in-house department or team.
This is due to one great benefit of Data Analytics if done by the right people: the ability to provide real-time feedback from customers regarding business transactions or interactions, among many other factors. Coupled with the rapid deployment of modifications to the business process, this could facilitate greater returns when customers feel that your business provides notable improvements in their experience.
One great benefit of Data Analytics: provide real-time feedback from customers and transactions
Related to the first entry on this list, some consider Data Analytics as something that is labor-intensive, it will need a dedicated group of people within your company to be done successfully. Although having a team solely devoted to Data Analytics can give important benefits, it remains a luxury to most companies, especially those who are not a part of the tech industry. Fielding a team is costly and time-consuming. Having to source, hire and onboard competent Data Engineers and Data Analysts, figuring out the right tools and automation frameworks to use, and all that on top of figuring out how to make use of all the information you're getting.
Big companies with large I.T. departments and established resource pools might pull it off, but most cannot. Smart businesses use third-party Data Analytics consultants that can perform an equal level of Data Analytics as internal teams, if not more. From visualizing data into digestible tidbits, configuring the algorithms that process data, to setting up Data Lakes to store data for processing, these service companies with years of Data Analytics experience can give you all the benefits of an in-house team for a fraction of the cost (and that’s not including the value-added services).
As with any endeavor that will require considerable amounts of brain-trust, results from Data Analytics can be maximized by having doctorate-level tech professionals leading the helm. These individuals often have the know-how to perform and gain actual insights from high-level statistical methods. The problem remains that it is very hard to find people who have advanced degrees related to Analytics. Even if you find one or two, it will take a significant amount of investment on a business’ part to take them on-board and work for you effectively.
A better alternative remains in outsourcing Data Analytics to external experts. They already have people who have knowledge and experience. it’s good to start looking for people who have a background in coding language, open-source software, and emerging techs like machine learning and A.I. Asking for their history with previous projects can also help determine if they had done projects that resulted in value-gains for their clients.
Data Analytics can be maximized by having postgraduate-level tech professionals leading the helm
With an abundance of Data Analytics and Emerging Technologies proponents come a minority of naysayers who are misinformed in these developments. What the dissenters don’t see is that these developments present new opportunities. With the effective use of Data Analytics, a business can ride the tide of AI. Companies can forecast market trends and discern customer behaviors better with automated predictive analytics. They could properly scale their workforce and integrate greater levels of automation into their business process.
The fact still remains, no matter how good the AI becomes in performing difficult tasks, they can never replace human intuition, creativity and ability to think outside of the box, all necessary attributes of a successful Data Analyst.
Some people think that just because Data Analytics needs automation to function, they can do away with human intervention. Often, the same people believe that the best algorithms are the only thing that can guarantee success in any Data Analytics project. AI can improve by leaps and bounds, but just like any other machine, there will come a time when it will need maintenance and updates through human intervention. Outdated algorithms will eventually return errors and an ever-increasing number of outliers that can skew results. This is where the human factor comes in. We can discern patterns and make hypotheses than can remodel systems. We can fix outdated systems or replace them. Ultimately, success in Data Analytics is achieved by making unique and creative inferences, a skill only humans can do.
It is not the tools nor the frameworks that come up with actual solutions, it is the people who do
You don’t need Big Data to do Data Analytics. The only thing that’s different from “ordinary” data and Big Data is its volume and complexity. Big Data is the data you collect among the millions, if not billions, of people around the globe using a service such as social media or video streaming. But data can be collected anywhere, in any quantity.
The key factor here is that Data Analytics makes use of Statistical Analysis, such as Multivariate Analysis and Regression, among others. These methods work with a sample size, not the whole population. This means you only need specific data that would return value, as against using all the data collected. Choosing specific data to use for Analytics comes down to utility and importance, and also the simplicity of how you format your data.
Every day, the number of tools, frameworks, and languages available are increasing and getting more complex. Figuring out what is the best mix of these things to successfully do Data Analytics is really complex, there are experts for these. People who have been doing it for a decade, if not more. And with experience comes skills. There are some Data Analytics companies today who have figured out the right formula for selecting the proper combination of tools and components to dole out actual value from analyzing data.
So it’s not the technology that’s difficult about Data Analytics. It’s applying the information gathered from the process, adjusting your strategies and business models to the incoming flood of insights and getting the right people to do it are the real difficulties one could face when doing Data Analytics. It is not the tools nor the frameworks that come up with actual solutions, it’s the people who do.
Is Data Analytics too slow? Advancements in computing power, Agile methodologies, and skills focused on fast and quality delivery, Data Analytics has reached real-time speeds. Critical questions regarding business operations or customer experience can now be answered in a matter of days, if not hours.
We have read that Forbes now calls data the “New Oil”, and there’s plenty of reason why that is true. Data has become the basis of a multi-billion dollar industry with giants like Amazon, Microsoft, and Google at the lead, with everyone else following behind. That’s why the world needs more data scientists, engineers and analysts now more than ever. The demand is worsened by the fact that there are not enough people in Data Science to go around. Thankfully, governments and private entities are realizing this and are mobilizing resources to develop more people into skills that deal with data.
Despite these developments, it is still difficult to find reliable people to do Data Science today. Finding people to fill jobs in Data Science teams is tedious at best. Innovators solve this problem by outsourcing. These companies find smart partners: Data Science experts who are able to work on data extraction, feature engineering, and prescriptive analysis. This allows them the benefit of cross-functioning some of their own people, like their Business Intelligence Specialists, to work with these partners, making them more productive. The bottom line is, it is not the number of people working on data that’s guaranteed to deliver. Making people answer the right questions will return the most value to your company.
That’s nine myths about Data Analytics we have debunked. Do you have any more to add? How would you argue against some of these misconceptions? Leave a comment below and follow our page for more posts like these.