5 Data Analytics Myths Obstructing Your Business Growth
In the last few years, the popularity and market size of big data has multiplied exponentially. According to experts, if data analytics solutions are designed and implemented properly, there's nothing much that could obstruct the success of a given project. From collecting data from multiple sources to sorting and analyzing how situations worked out in the past and what could the future possibly hold, big data can be treated as indispensable for all enterprises alike.
However, there are still a lot of business leaders around the world who are reluctant about introducing data analytics in their organizations. Although the fact that big data can help companies spot hidden market trends and customer behavior patterns and offer them a competitive edge has been accepted by many, there's still a handful of severe misconceptions around the concept. In this blog, we will be debunking the top 5 data analytics myths before they start hindering your company’s growth.
Myth 1: Data Analytics Is Expensive
One of the biggest fallacies surrounding the implementation of data analytics solutions in an organization is its cost. This prevents innumerable business leaders from acknowledging and exploring its benefits. The truth is, by bringing data analytics into the picture, you can cut down the costs of three fundamental areas namely, data collection, data storage, and various software systems.
- Data Collection – Both structured and unstructured data can be collected from distinct sources using IIoT platforms and devices with an active internet connection.
- Data Storage – Storing and accessing data from a common cloud platform can reduce the additional costs of compliance and subscription charges in case of more than one user.
- Data Analytics Software Systems – Instead of investing in multiple standalone applications for taking over the different stages of data analysis, you can have only a software system that accomplishes all of the required tasks.
The expenses associated with data analytics largely depends on the solution your organization is choosing. When it comes to tangible profits, even if the implementation seems a little expensive, the prospect of better decision-making for maximizing ROI cannot be overlooked.
Myth 2: Data Analytics Requires Large Volumes Of Data
When working with a powerful tool like Microsoft Power BI, you are not obligated to produce large volumes of data for spotting hidden trends and generating valuable insights. There's no denying that predictive analytics work better when there are diverse and colossal amounts of data available at but, that is far from being the bottom line. Even if you have about 1000-1500 reports at your disposal, implementing data analytics wouldn't invite any further struggles.
As opposed to the belief that the quantity of data makes all the difference in data analytics, it is essentially the credibility and relevance of the information that matters the most. Seeking assistance from one of the top analytics consultants and asking them the right questions to fabricate a customized strategy for your organization can go a long way.
Myth 3: Data Analytics Doesn’t Have To Be Paired With Manual Labour
As data analytics is based on machine learning, artificial intelligence, and automation, business leaders tend to deem that it eliminates the need of appointing a human workforce. Although we dismiss the fact that data analytics reduces the burden of performing repetitive tasks over and over again, it cannot replace the contribution of manual labor.
At a time when technology is capsuling almost every imperative facet of our lives, treating digital disruption as a threat can turn out to be a major threat for your company. As far as data analytics is concerned, it is the right approach to leveraging augmented intelligence that will keep the balance intact. Making way for a collaborative culture between humans and technology is the only solution for being future-ready.
Employees can transfer the load of conducting time-intensive analytical tasks to automation and focus on the more challenging errands that call for human interference. Machine-based analytics is responsible for generating critical insights and spotting latent consumer behavior patterns while it is a team of human agents that would be interpreting and putting them into use.
Myth 4: Only Enterprises Need Data Analytics
If you are running a small or medium-sized enterprise, you might be under the impression that data analytics is only meant for large enterprises and multi-national companies. Truth be told, the efficacy of data analytics isn’t influenced by the size of a company, rather its requirements and goals. Analytical insights can serve you with a clear idea of your customer expectations and help you identify the prevailing issues in your business processes. Also, with a self-service analytics tool like Power BI, the SMBs can steer away from hiring highly-skilled IT workers and data scientists. This automatically removes the part of the extra investment as employees, despite their technological competency, can use the application for improving and streamlining business operations.
Myth 5: You Must Hire a Team Of Data Scientists For Analytics
Gone are those days when building a strong and effective data analytics model and structure required the most talented data scientists of the industry. Bringing together an efficient team of data scientists can always render you with a competitive advantage but, as long as you are not looking forward to launching new products and applications, you can do without them.
To function smoothly without a team of data scientists, you must outline your data analytics anticipations according to the existing data quality and talent. Additionally, by getting in touch with an experienced data analytics consultant, you can equip your team with high-quality data and strategies for seamless interpretation of the insights generated by joint efforts.
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