By 2025, the market for business intelligence will be worth $33.3 billion, and 62% of retailers say that data analytics and insights provide them a competitive edge. Business intelligence and predictive analytics are interchangeable, yet they are not the same.

You are probably not the only one curious about the distinction between business intelligence and predictive analytics. There are differences between the two phrases, albeit business intelligence is the more inclusive term that includes analytics.

What is becoming lost in translation as businesses large and small join the big data arms race is the method — and distinction — of acquiring business intelligence and predictive analytics to make actual business choices with an impact. Brands should know the correct process to apply to their systems throughout their operations.

Business Intelligence vs. Predictive Analytics

As per Clootrack’s CX report, 14.8% of CX professionals think one method to enhance the customer experience is by examining data and trends.

"Brands should implement robust 'customer listening' tools across all critical customer touchpoints-use a combination of surveys/interviews/focus groups + data mining techniques to tap into transactional systems (e.g., ERPs, CRMs, Customer Service technologies, etc.) to build a 360-degree view of your customer's journey with your organization," Julie Ryan, Director of Global Customer Experience, Johnson & Johnson in Clootrack’s CX report.

Global interconnectivity is growing together with the pace at which the world is digitizing, producing a large amount of data. This data can assist businesses in making more informed decisions and provide customers with better, better-personalized products and services.

However, despite having access to a wealth of data, many businesses are unsure how to make the most of it. The essence is in using advanced analytics, which supports sound judgment and gives decision-making authority to personnel most competent. Business intelligence and predictive analytics are two analytical techniques that can help firms make the most of their massive data sets.

What is Business Intelligence?

Business intelligence analyzes collected data and explains what should be executed in specific circumstances. More precisely, they evaluate potential circumstances or scenarios, previous and present performance data, and available resources to suggest the most appropriate action.

AI and ML can weigh decision outcomes rather than scenario outcomes without extra manual intervention. Business intelligence can help organizations choose the best options with these technologies. Business intelligence has a wealth of beneficial use cases for organizations with large amounts of data. Businesses in sectors such as financial services, healthcare, or retail where human error is commonplace stand to gain significantly from the steady helping force that business intelligence gives to the company's overall strategy.

For instance, guided marketing, sales, and pricing are the three main aspects of business intelligence in the retail industry. Business intelligence directs retailers toward the greatest things to provide, the most effective ways to promote them, and the prices to offer at any particular time. As a result, consumers have been prescribed the right product at the right moment, together with engaging content.

What is Predictive Analytics?

On the other hand, with the help of sophisticated algorithms and large amounts of historical data, predictive analytics can give organizations accurate predictions. This type of analytics can forecast results for several "what-if" scenarios by utilizing technology such as artificial intelligence (AI) and machine learning (ML).

Business leaders can use these forecasts to help with major decisions. Predictive models also give businesses the ability to estimate the possible reward or risk presented by certain circumstances.

Companies can quickly adapt their product strategy and respond to shifting market conditions thanks to predictive analytics technologies. With predictive analytics' actionable insights, they may more easily identify the new potential for generating profit, boosting sales, and enhancing customer retention.

How to Choose the Right Approach Between BI & Predictive Analysis?

Although both business intelligence and predictive analytics are powerful tools, their synergistic effects can assist in transforming descriptive measurements into the most easily understandable rules of thumb for making decisions.

You cannot make wise choices without the proper data on to base your conclusions. Business intelligence is all about using the correct technology to visualize data in a meaningful way and then distributing that information to the appropriate audience at the appropriate time. In other words, for businesses to perform better than their competitors, understanding and analyzing both previous and future circumstances might be helpful.

Ensuring Data Sufficiency

Businesses seeking to combine business intelligence and predictive analytics should ensure their data is comprehensive and rich to account for all potential outcomes.

Results will be better if information sets are created with more depth of data, taking additional variables like age and gender into account.

This is because analytic sequencing can forecast and prescribe actions based on real-time successes and failures thanks to business intelligence and predictive analytics models that are updated regularly with the most recent numbers.

What Are The Benefits Of BI And Predictive Analytics For Your Business?

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Effective use of business intelligence and predictive analytics makes all the difference in the world between successful and unsuccessful businesses today. Why? Every industry is changing and getting more competitive, therefore using business intelligence and data analytics effectively is essential to surpassing the competition.

For example, typical advertising techniques in marketing, such as spending a lot of money on TV, radio, and print commercials without considering ROI, are no longer as effective as they once were. Advertisements that aren't specifically targeted at them are no longer being tolerated by consumers as much.

The businesses that are the most effective at both B2C and B2B marketing create hyper-specific campaigns that reach out to targeted prospects with a tailored message using data and online BI tools. Everything is tested, and the successful efforts receive further funding while the unsuccessful ones are not repeated.

Some use cases of BI and predictive analytics: 

  • Identifying existing and potential bottlenecks 
  • Improving operations and processes
  • Planning better business strategies
  • Churning crucial business data into rich insights
  • Targeting more profitable/streamlined business outcomes

In the end, business intelligence and analytics are much more than just tools for data collection and analysis. They center on adopting an experimental attitude and being open to letting data inform a company's decision-making process.

How to Implement the Right Approach Towards BI & Predictive Analysis?

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You need well-defined problems and a method for tackling them. If your company isn't set up to act on the data you find, all the intelligence in the world will be for nothing. Go through the ways to implement the right approach using this 6-step guide:

1. Clearly Define the Problem

You need a strictly delineated problem that can be handled with these advanced analytics to make the most of your organization's business intelligence and predictive analytics tools. The most shrewd businesses prioritize scaling business intelligence and predictive analytics with a single problem in mind. Organizing your board, executive team, and IT department around a single issue that could make a significant difference is considerably more effective. Start with a challenge that is well stated and set logical, quantifiable, executable, achievable, and timely goals.

2. Identify the Stakeholders 

Poor performance gets minimized with proper planning. What details are they in need of? What will they do with it? What information will be provided by the data? Companies risk making decisions that don't ultimately help the business as intended if they don't match their method of gathering and interpreting data with business intents and priorities.

Predictive analytics and business intelligence may involve software, but it doesn't mean they are exclusively IT projects. Financial data is necessary for business intelligence and predictive analytics, but this does not mean that the finance department should be the only one concerned. Moreover, if your company is modest, you can hire new employees or assign individuals to perform multiple tasks. You should execute the following measures to educate and bring in your workforce:

  • Invite a participant from each team impacted by your plan.
  • Interview them and include them early.
  • Find out how they use data in their work, what is effective for them, and what is not.
  • To customize the scope of your implementation, use these insights.

Business intelligence and predictive analytics platforms ensure that dashboards and reports are understandable and accessible to non-analysts. However, you will need this cross-functional team to carry out the plan to get the platform in place and functioning properly. Human resistance to change is one of the initial problems with implementing predictive analytics and business intelligence. Educating your staff is the most efficient approach to reducing opposition. If your firm has never used predictive analytics or business intelligence, you must describe how each department would profit from the deployment. 

3. Decide What Data You Require and How to Obtain It

It is impractical to expect 100% pure, high-quality data, but you must have a data management strategy in place to make this work. Good business intelligence and predictive analytics result from good data input. To ensure uniform business requirements throughout the range of internal processes, analytics and business teams must collaborate on analytics solutions. This also makes it essential for the teams to focus on the right sources to obtain data. 

The quality of predictive analytics depends on the data it uses to make predictions. To provide correct assessments, it is crucial to aggregate and clean up data from many data sources. The most important reality is that no single piece of data should be permitted to claim undue influence. The data needed for predictive analytics is usually a mixture of historical and real-time data. These sources can either be internal or third-party points of data collection. The data can be further structured or unstructured. 

4. Determine KPIs to Gauge Success

"When it comes to data and analytics, brands should take the time to set up the proper data sets and track the right key performance indicators (KPIs) that drive the success of their business. They also need the talent, whether internal or a third-party partner, to take that data and tell a story with it. Data can identify pain points, customer journeys, conversion rates, and more. Talk to the retailers you are selling to and get more information from them. Partner with retailers to get analytics on interactions with your products from the consumer standpoint. That will give you more insight on how consumers feel about your product, demographics on who is buying it, and gives you the opportunity to target the customer experience to dial in on that feedback," Kaela Kucera, Ecommerce Manager, Pierce Manufacturing in the Clootrack report.

What is measured is controlled. Once you've identified the key people in each area, assemble and rank the company's pain points and key performance indicators (KPIs) with their assistance. You need a method that can be used objectively to evaluate the efficacy and success of your launch. Determining the KPIs, you will track for the entire organization and the KPIs to track inside the departments is crucial once you have acquired adequate data.

As a result, create key performance indicators with your team that everyone will support. Your KPIs should be quantifiable, aligned with your aims, and essential to attaining your organizational objectives. Some vital KPIs could be conversion rate, customer acquisition cost, net promoter score, customer effort score, etc.

5. Translate Data Into Action

Streamline reporting. Set deadlines for acting on the information and develop systems and procedures that automatically provide information to the appropriate individuals at the appropriate time. Adopting new analytical tools by a business organization necessitates a significant change in the mindset of workers and their performance expectations. Organizations frequently don't put enough attention on preparing managers and staff for an advanced data-driven shift. Such a shift revolves around obtaining rich insights from large data sets by funneling through business priorities and customer goals. As a result, businesses get a strong grip on ongoing customer behavior patterns, concerns at various touchpoints, positive indicators, etc.

For example, in Clootrack’s case study, see how a bank uses data and analytics to improve customer experience. Analyzing data is an important advantage for fast-growing companies to be able to quickly adjust their strategies, so their stakeholders are able to make important decisions and take actions based on customer data. 

6. Implement the Pilot Project 

It's time for a test run as soon as all of the business intelligence and predictive analytics processes are prepared. Furthermore, even if testing the system on a corporate level could sound like a brilliant opportunity, running a pilot project with a smaller group is preferable.

Review your results to evaluate if you lived up to initial expectations. If not, figure out how to meet the initial KPIs. Perform another pilot after making the changes to determine how much ground you've covered between the two pilots and how the situation has changed. Implementing business intelligence and predictive analytics is a continual process, and every run needs to be optimized until everyone is satisfied with the outcome. You can safely scale up once you get there. 

The Key Takeaway

Predictive analytics is more focused on future predictions and trends than business intelligence, which assists employees in making decisions based on historical data. This is the main distinction between the two. Businesses will gain actionable insights that enable better decision-making and improved risk management. Business intelligence and predictive analytics models can be crucial components of an ideal business plan when used jointly and strategically.

The trick is to stick with the plan and put in the required effort and money. Both business intelligence and predictive analytics investments may be quite cost-effective in the long term.

Read More: 7 Attributes Of a Highly Data-Driven Organization