Data-centricity has become a necessity for companies today to secure a future. Everyone’s chasing it, and those who haven’t been will soon be left behind. Companies are collecting data all around and trying to make sense of it with analytics. But with all this concentration on data, companies are making a big mistake.
The sun is rising on Artificial Intelligence (AI), which has become a buzzword, and that too for a good reason. But in the midst of making sense of all this data, companies have been quick to jump to the conclusion that they can solve every problem with AI. This misconception will hurt those dearly who don’t do their background work on what they are trying to do or what problem they’re trying to solve.
Companies need to consider a data analytics solution as prescriptive and descriptive analytics can solve most of the problems without AI. That should be a relief to most because implementing AI burns a hole in your wallet while you carry the risks along with it.
AI is a powerful tool that makes a huge difference if you utilize it. C-level executives may feel pressurized to implement operations without fully understanding what it can or cannot do for your business.
So before you go on trying to jump on the AI bandwagon, there are many things you need to consider first:
Even when you’ve answered these questions for yourself, know that AI isn’t a silver bullet solution for all your problems. It’s only wise to weigh all your options before initiating something. Most of your problems can be solved using simple prescriptive and descriptive analytics without the need for AI.
One describes past events, while the other focuses on making recommendations for the future. Combined, they can be a powerful tool in helping companies determine their future course.
You can gain insights into why something happened in the past through descriptive analysis. Suppose you want to figure out the answer to questions such as:
“What are the characteristics of high-performing sales teams?”
This can be identified through descriptive analysis of past data by analyzing historical sales data of the behavior performance of a sales team, as well as other factors that would contribute to their success.
You can also get answers to questions like “How many customers visited in the last quarter, and what are their churn rates?” Further, you can employ the data to improve your current strategies and form KPIs to improve your overall performance.
Gaining insight for determining the best course of action for the future is Prescriptive Analysis. This method determines the best course of action for the future. Here data is utilized to make recommendations and guide on what actions to take.
Questions of a predictive nature are answered in this, e.g.,
“What marketing campaign will result in the highest return on investment?”
“What changes should we make to our product or service offerings to better meet customer needs and preferences?”
After carefully determining the needed variables, a company can make great use of prescriptive analysis for its decision-making and steer itself in the right direction.
Prescriptive and descriptive analysis are powerful tools, and you don’t need the implementation of AI and machine learning to get you there. In many ways, using these is better than getting fixated on implementing AI. Why? Well, certain risks come along with AI.
If a company is going to use AI for things that are easily implemented through readily available procedures like prescriptive and descriptive analysis, they’re just losing value for money.
Today more than ever, enormous amounts of data is being shared everywhere. The data is often sensitive and personal. If not handled properly, it can lead to a privacy breach. This is one of the risks of artificial intelligence that you must consider seriously.
To secure this data, you’ll have to introduce encryption modules that involve further data warehousing fees and other unnecessary costs.
AI requires a huge chunk of data to churn out viable results. If the data is biased, the algorithm will be biased as well. These unintended consequences can negatively impact a business. AI models can produce inaccurate and biased results without vast amounts of data.
Every decision is made for the company's betterment and financial profitability. Using AI just for the sake of it isn’t wise and requires a lot of resources. Implementing a whole AI module for a specific purpose can be expensive. There are several costs that executives need to consider before thinking of implementing AI.
These costs include:
It simply doesn’t make sense for a company to go through all of these costs to implement something which data analytics can do without AI. It isn't good value for money unless you plan to set up your own generative AI platform.
AI is a great tool that makes our work faster, but it’s not a one size fits all solution for business problems. When companies do their research right, they’ll understand that prescriptive and descriptive analytics can solve many problems as effectively and be significantly more cost-effective.
Companies need to gain insights into the importance of big data analytics, as they can provide valuable insights to help companies improve multiple aspects of their business. Both, The company executives' and data analytics' ultimate goal is to drive business growth and success. So be sure to choose the right tools for the job.