3 Data Analytics Myths That Need Busting

Many organizations today are starting to lean on data analytics to streamline their processes and increase profit margins. Companies that are interested in data analytics but don’t have a system in place shouldn’t be intimidated. Using data wisely isn’t as treacherous as it seems.

There are plenty of myths surrounding data analytics, including the cost to implement new hardware and infrastructure, the accuracy of the data and the need for data scientists to run an analytic program. Without digging beneath the surface, these assertions alone could be the difference of a company achieving value through data and a company still making business decisions on hunches and anecdotal evidence.

Let’s take a look at these claims in more detail to cut through the misconceptions companies have when it comes to weaving data analytics into everyday organizational workflows.

Data Analytics Costs Too Much – FALSE

any large companies have major resources to invest into hardware and infrastructure for data analytics, but not all companies need to invest big bucks to see value from data Plenty of options exist for companies not looking to empty the bank on data analytics, and I’m not just talking about cloud-based solutions.

A major burden of adopting any data analytic platform is justifying cost and proofing value. That’s why it’s so important to adopt a platform that provides value immediately through being user-friendly, scalable and makes insights easy to share across the company. More platforms are emerging that check off all these boxes which accelerates what is usually months spent in POC.

Some cloud-based platforms certainly check these boxes, though. According to insideBIGDATA, cloud platforms s reduce the costs and complexities usually associated with data scattered across multiple systems and platforms, along with independence from local IT on-premise infrastructure constraints. Most of these cloud-based analytics providers give companies an opportunity to test the product before buying it. Setting a budget for data analytics and measuring the investment against the benefits it provides your organization can go a long way to assessing its value.

The Generated Data Isn’t Accurate Enough – FALSE

Using data analytics doesn’t ensure perfection, but the tools are there for companies to accurately collect information and perform tasks. A lot of the time, the inaccuracies come from input mistakes or human bias. These inefficiencies can come from the users not knowing exactly what they have at their fingertips or through multiple data collection channels that are siloed and sporadically updated. For someone who is not a data analytics mastermind, it can be quite easy to overlook errors or redundancies. Data analytics are similar to an old hard-work adage, but in a more literal sense because you literally get what you put into it.

Some data analytics platforms like ThoughtSpot automatically mine data and present findings through dashboards and digestible interfaces. Since these kinds of platforms are user-friendly and transparent in sourcing their findings, employees can have peace of mind that they’re working with accurate, relevant data.

Data Analysts Are Required for Data Analytics – FALSE

Just because an organization is going to spend more time and money on their data analytics does not mean that they need more data scientists. According to Business Intelligence Best Practices, you no longer need people with formal training in statistics or the ability to program logic. Today’s analytic workbenches make it possible for analysts with some statistical knowledge, basic database skills and a keen understanding of the business and its underlying data to create effective models. This allows companies to take their analytics processes one step further and continually optimize data workflow processes and evaluate how data is working for the organization.

For organizations that aren’t currently using data to their advantage, there’s a lot to look into as far as cost, accuracy, ease of use and the type of people needed to run the system. Before trying to implement an analytic system though, these organizations need to make sure they’re doing their research and not being scared off by half-truths and exaggerations.

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