Today, Data Analytics is one of the most exciting trends across industries and has proven its effectiveness, beyond the traditional scope, in driving a company’s profits. Several online data analytics courses offer you the luxury to learn at your own pace without compromising on the quality of education. Even as businesses are becoming more data-driven, there are several loopholes that prevent organisations from leveraging data analytics to the maximum.
Below, we list down 12 ways in which companies can use data analytics to drive lucrative business outcomes:
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View failure in a new light
People, and by extension companies, see failure as an impediment to success. However, that mentality is slowly shifting to incorporate the healthy notion that ‘failure is a stepping stone to success,’ as it is our failures that tell us what is lacking, which areas in the organisational plan need improvement and how to avoid making the same mistakes again.
Indeed, we can learn a great deal from failure, and data tells us better than anything what is working and what is not – instigating that learning.
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Think outside the traditional approach
Data science is perceived as a field where innovation and statistical science drive the best outcome for an enterprise.
However, analysts/scientists are usually stuck in the mindset of only measuring a company’s performance instead of finding scientific ways to optimize that performance. Hence, companies wishing to climb the corporate ladder should have a more data-centric approach that can exhibit an all-round influence on the business actions.
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Integrate manpower with smart tools
The dearth of qualified data professionals in the market can be attributed to the lack of competitive data analytics courses.
But this has also given rise to the development and use of self-service tools (to replace manpower as much as possible), which reduce the need for time-consuming efforts on the part of professionals and monitoring by supervisors.
With the mundane tasks being handled by automated services, the skilled data scientists can use their skill-sets to solve real business challenges.
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Unique approach to problem-solving
Anthony Scriffignano, Chief Data Scientist at Dun & Bradstreet, feels that,
“Any conversation that starts with tools is the wrong conversation. You should start with a business problem.”
Data analytics focuses on this unconventional approach to problem-solving and it has proven to be very effective.
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Engage data strategy with business plans
Data alone fails to be informative if we cannot apply it to the current business situation.
Data science, combined with the context of business operations, can help scientists leverage the available information to drive better business outcomes. Thus, scientists can present the C-levels (senior management) with a resolution that aligns the analysis with the business goals.
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Designate a specific place for the data team
As the data team is a new concept, where to place it in the system or organisation is highly debated.
Some organisations prefer to place it in the IT department or to have a new service organisation like Human Resources. But arguably the best solution is to have the data team as a hybrid, connecting and communicating effectively with other departments.
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Integrate into the organisational mindset
Usually, in an organisation, data science does not permeate the everyday conversations of the office.
Data analytics should be made a part of the problem-solving approach of all teams as it can open up interesting avenues that can better the business output immeasurably.
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Adapt to the changing times
The workings of an organisation have become more virtual and hence, to sustain and grow a business, companies need to integrate changes in real-time to stay relevant in the market. Companies are utilizing the enormous advantages offered by data analytics to upgrade their working style and are reaping its benefits.
From political campaigns (think Hillary Clinton vs Donald Trump) to expanding tech startups and even established MNC’s across sectors are employing multiple data analytics tools, techniques and knowledge to scale and succeed.
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| SL. No | Top Data Science Skills to Learn | |
| 1 | Data Analysis Programs | Inferential Statistics Programs |
| 2 | Hypothesis Testing Programs | Logistic Regression Programs |
| 3 | Linear Regression Programs | Linear Algebra for Analysis Programs |
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Embrace new technology
Many organisations have adopted BI (Business Intelligence) and analytical tools with hopes to integrate it into the tools that their employees’ use on a regular basis. But imposing the use of data or analytics will not do the trick.
The management should emphasize its benefits and ensure its organic adoption.
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Employ contrasting views
Any problem, when looked at from a single perspective, can only provide a limited solution. Hence, the load of data that undergoes the first, second, third order observations, reveals patterns that may have gone unnoticed the first time around.
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Take a detail-oriented approach
With time, clients have become more detail-oriented and want to narrow down the reasons for a sales hike and don’t just want the big picture. Data analytics offers predictive analysis with a break-down of the buying patterns of the customer.
Take for instance Netflix and Amazon and how they use data to predict buying behaviour:
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Begin from the ground-up
Be well-acquainted with your shortcomings and what does not work as it can lay the groundwork for your next attempt. Always start with a business problem, work out the details, fix the problem and measure your rate of success.
You have plenty of chances as this is a trial-and-error process.
If the potential of data analytics is used by managers and large teams alike to enhance business outcomes, there will be no stopping businesses from achieving the impossible.
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