Data-centricity has become essential 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 from all sources and trying to make sense of it with analytics. But with all this concentration on data, companies are making a big mistake.
Artificial Intelligence (AI), once a buzzword, is gaining prominence and that too for a good reason. But in their eagerness to leverage data, companies often assume AI can solve every problem. This misconception can severely hurt those 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.
The Power of Prescriptive & Descriptive Analytics
Business Analytics is the process by which businesses use statistical methods and technologies for analyzing data to gain insights and improve their strategic decision-making.
There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future. Whilst each of these methods are useful when used individually, they become especially powerful when used together. If combined, they can be a powerful tool in helping companies determine their future course.
Data analytics strategies stemming from these core procedures is impacting several industries in the modern era. There are 10 major industries that are benefitting from it.
Descriptive Analytics
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 a sales team’s behavior performance, and 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.
Here are some of the major benefits of descriptive analytics:
- Ease of Use: Descriptive analysis is straightforward and doesn’t require extensive expertise in statistical methods or analytics.
- Abundance of Tools: Numerous analytics tools are available that handle much of the work, making descriptive analytics easy to perform.
- Answers Key Questions: Descriptive analytics effectively answers common business performance questions, providing essential data for stakeholders to assess and improve. It helps stakeholders answer questions like “How are we doing?” or “What should be differently?”
Descriptive analytics helps businesses get a clear view of their historical performance and operations. This approach is best for organizations auditing the effectiveness of their past tactics and strategies, identifying behavioral patterns in their customers, and assessing their overall business performance over a specific period.
An example of descriptive analytics in action can include an e-commerce company using descriptive analytics to examine past website traffic, user behavior, and conversion rates. By analyzing these metrics, the company can identify popular products, understand user navigation patterns, and optimize the website layout to enhance user experience and increase sales.
Prescriptive Analytics
Prescriptive analytics is an advanced data analytics model that delves into data and provides insights on what actions need to be taken to achieve desired outcomes. Unlike descriptive analytics, which summarizes historical data, prescriptive analytics takes it a step further by suggesting various action plans and highlighting the implications of each option. All necessary data is effectively utilized to make recommendations and guide businesses on what actions to take.
Questions of a predictive nature are answered in this manner, such as:
- “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.
Some of the benefits of prescriptive analytics include yielding:
- Actionable Insights: Prescriptive analytics turns raw data into specific actions, ensuring decisions are data-driven and free from bias or emotion.
- Optimized Decision-Making: By evaluating different scenarios, prescriptive analytics helps companies choose the best actions and predict outcomes, enabling informed decisions that align with goals.
- Enhanced Operational Efficiency: These tools identify bottlenecks, streamline processes, and improve resource allocation, making daily tasks more efficient and cost-effective.
- Forward-Looking Strategy: Unlike other analytics, prescriptive analytics focuses on the future, providing a roadmap to prepare for challenges and seize opportunities.
- Adaptability in an Evolving Market: Prescriptive analytics offers real-time adjustments based on current data and market trends, keeping organizations agile.
- Increased Profitability: By guiding strategic decisions and optimizing operations, prescriptive analytics can boost financial performance and profit margins.
Prescriptive analytics is particularly valuable for businesses aiming to forecast future outcomes and identify the best actions to achieve their goals. It is essential for organizations looking to optimize complex decisions, strategically allocate resources, and navigate scenarios with multiple variables.
An example of prescriptive analytics in action can include a manufacturing company can utilize prescriptive analytics to improve production efficiency. By analyzing machine performance data, maintenance schedules, and production goals, the system can recommend adjustments to production schedules, maintenance routines, and resource allocation to maximize output and reduce downtime.
Descriptive and prescriptive analytics 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.
Things to Consider Before Investing in Artificial Intelligence
If a company is going to use AI for things that are easily implemented through readily available procedures like prescriptive and descriptive analytics, they’re just losing value for money.
Data Privacy & Dependency
Privacy
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.
Pro Tip: Cybersecurity can serve as a solution for a scenario like this one. Click here to read more about cybersecurity and how it can alleviate your privacy concerns.
Dependency
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.
Uncertainty in Getting Value for Money
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:
- Data acquisition and preparation
- Infrastructure and Hardware
- Software and licensing
- Training and development
- Integration and customization
- Maintenance and support
- Legal and regulatory compliance
It doesn’t make sense for a company to go through 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.
The Bottom Line
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 must recognize the importance of big data analytics, which can offer valuable insights to enhance various aspects of their business. The ultimate goal for both company executives and data analysts is to drive growth and success. Therefore, it’s crucial to choose the right tools for the job.