The value of data has increased multi-fold in the post-pandemic digital-first economy. As Business intelligence (BI) teams aggregate internal and external data in zettabytes, analysis is becoming a challenge. They are struggling to derive positive value from such data initiatives for various reasons including:
- First-generation data analytics tools can no longer cope with the ever-increasing volumes of data.
- Traditional business intelligence has become outdated. Businesses can no longer derive competitive advantage from such tools.
- BI analytics is historical, extrapolating trends based on past events. Such extrapolations no longer work today. For instance, COVID-19 has changed customer preferences, rendering most historic data obsolete. The supply chain disruptions and inflationary trends that followed caused further upheavals. Geopolitical disturbances such as high oil prices and the war in Ukraine caused further disruptions.
Deriving effective business intelligence in an uncertain world requires the help of AI.
The World Economic Forum estimates Artificial intelligence (AI) will replace about 85 million jobs worldwide, by 2025. AI, in turn, will create about 97 million new ones. McKinsey estimates that more than half of global business leaders have adopted AI in at least one business function.
But isn’t business intelligence and artificial intelligence the same thing? Both BI and AI extract insights from data to improve business performance. Many people use these concepts interchangeably. But the two are not the same.
The difference between Business intelligence and artificial intelligence tools
Business intelligence platforms streamline collecting, reporting, and analyzing data. Classical BI interprets past events to predict trends. BI tools tracks business activity and offers the data in easy-to-consume KPIs, spread sheets, performance metrics, dashboards, charts, graphs, aggregated reports, or other visualizations. These focus on what was and is, but not on what needs to be done.
A robust BI platform arranges messy data to give a coherent picture but lack the capabilities to enable follow-up action. Also, it cannot drill down insights to the level of an individual customer.
Artificial intelligence tools use data to model human intelligence, including human behaviors and thought processes. Machine Learning algorithms learn from data and make rational decisions. These tools analyze data deeper and make predictions without specific instructions.
AI tools fill the gaps and shortcomings of BI. AI starts from where BI ends by enabling computers to make business decisions at a personalized level.
BI is hypothesis-driven and generic. AI is specific and enables a personalized approach. BI tools improve the data quality and make the process consistent. AI tools unlock insights from such data.
How AI Enriches BI
Co-opting AI-powered predictive analytics gives sophisticated new capabilities to BI tools. The confluence of AI and BI allow businesses to synthesize vast quantities of unrelated data into coherent plans of action.
Business intelligence platforms offer great visualizations, but leave it to the user to make interpretations from these visualizations. Artificial intelligence plugins generate insightful summaries within seconds. It offers easy-to-understand narratives, enabling users to make informed decisions.
AI enriches business intelligence platform in the following ways.
1. More advanced and sophisticated analysis
Business intelligence platforms organize, analyze, and visualize data for businesses. These tools do the task with greater efficiency and depth than spreadsheets or other basic tools. But deriving insights from it is primarily a manual process and requires technical expertise. Even then, the insights depend on individual perception.
AI-driven analytics identify patterns not visible through conventional dashboards or spreadsheets. AI enables BI tools to produce more precise and better insights. The AI-enriched system
- Makes analytics broad-based. The AI-powered system pulls information from a broader data set, including extraneous data sets. AI algorithms learn from the deepest level of historical data and forecast based on new data to detect the most relevant points.
- Clarify the importance of each data point at a granular level. Business users understand how any specific data set translates into real business decisions.
- Enable personalization. Generic promotions or even advertisements no longer entice customers. Today’s customers are informed. They expect personalized engagement. Businesses looking to attract customers have to roll out personalization initiatives.
- Enable process automation. Users can configure to update customer information and other key records automatically. Cognitive insight applications are the advanced version of process automation. Here, the application learns and improves with every interaction. Automation also allows error-free, precise, and standardized reports.
- Co-opt emotions and other unstructured data. AI enriches BI by co-opting unstructured data to analytics and generating better insights. Consider AI’s computer vision capabilities that co-opt customer emotions to analytics. A study conducted at IbnTofail University correlates decision making with facial expressions. The study predicted the customer’s preference for products with high accuracy. The study allows marketers to predict the customer’s decisions based on their visual cues. Such insights optimize the effectiveness of ad campaigns.
- Optimize processes. Applying predictive analytics delivers efficiency improvements and eliminates waste. In the retail value chain, it optimizes inventory, logistics, sales, and pricing.
2. Enabling informed decision-making
Artificial intelligence extracts business value from data. In a recent PwC research, 54% of company executives believe that artificial intelligence improves decision-making.
One of the key applications of informed decision-making is in sales and marketing. BI tools make explicit historical customer buying. AI takes such data, adds relevant extraneous data, and forecasts customer needs.
The key benefit AI brings to the BI process is predictive analytics. Predictive analytics make it easy to structure marketing and sales roadmap and set KPIs or business metrics. Advanced statistical algorithms analyze relevant information in real-time and suggest the best course of action.
AI-powered predictive analytics:
- Empower business users. BI tools already enable users to get consumable insights. AI offers enriched insights without data scientists explaining the output.
- Allow deploying resources to higher-value activities. Consider integrating AI into CRM. The sales team gets specific insights, such as which customers will bring the most profits and who will probably churn. Marketers may focus their time and energy on high-value customers.
- Enable specific interventions. AI allows businesses to create targeted marketing or personalized promotions. For instance, BI only makes the connection between customers receiving special offers and returning to buy again. It does not go to the specifics of which customers will like a particular offer at a specific moment in the future. With AI-powered predictive intelligence, businesses can customize promotions. They do not have to make generic offers to a large cohort of customers and hope a few will respond. Instead, marketers apply predictive intelligence to identify customers likely to return on their own, and others who need promotional offers. They may further drill down to the type of offer that will entice specific customers.
3. Enabling more proactive decisions
Traditional BI tools use historical data and highlight past events rather than future ones that matter. This leads to a culture of reactive, data-driven decisions. Adding AI’s predictive analytics capabilities improves data-driven decision-making. It enables more forward-thinking and innovative choices.
- Preventive actions. AI-powered analytics enable proactive actions that prevent damage or improve outcomes. For instance, medical providers use BI to track re-admission rates. Adding AI helps to identify patient subsets with a high risk of re-admission and can proactively take steps to reduce the percentage.
- Dynamic metrics. Business intelligence platforms gather customer data from different interfaces and touchpoints. These platforms aggregate data from emails, chatbots, and social media and present it in a cohesive, unified format. Traditional BI tools rely on static and sometimes arbitrary business rules. For instance, the BI tool classified a lead as “cold” if there is no communication in five days, a random value. Such arbitrary and static business rules distort insights and hurt competitiveness. AI enables using a far more comprehensive range of data points and real-time analytics that gives a more accurate picture. Artificial intelligence enables dynamic and definite metrics to measure success. For instance, AI tools analyze millions of data points. The analysis may show the lead going cold if not contacted within two days. This way, AI tools reduce missed opportunities.
- Speedy decision-making. The insights delivered by AI predict trends, optimize logistics operations, and set pricing. Businesses may adjust supplies according to perceived demand. They may even send products for delivery without waiting for order confirmation, as Amazon does.
- Translating insights to action. Business intelligence solutions can explain a problem. But AI can do something to resolve the problem. AI unlocks several data based use cases that takes enable businesses to convert their business intelligence into marketable products or services. For instance, BI solutions may identify the problem of poor sales as delays in releasing items from the inventory. It requires AI solutions to identify and implement solutions that resolve the problem. The best example of AI in action is recommendation engines that collate data on users and others browsing history, customer behaviour, product information, buying patterns and more.
Obstacles to overcome
Using business intelligence and artificial intelligence in tandem offers great benefits for the enterprise. But deployment in tandem, and establishing synergy is a tough task. Here are the key challenges that stand in the way of businesses using AI with BI to maximize business outcomes.
- Challenge of accessing technology
Business intelligence platform has been the toast of several industries in the last couple of decades. But artificial intelligence and machine learning have become popular only in the last five years or so. AI technology is still nascent, and many businesses do not adequately understand the technology and the benefits from it. AI is very compute-intensive, and the high costs of setting up AI infrastructure deterred many businesses. Also, the talent shortage plaguing the IT industry for over a decade now is the worst in data science. Skill development has not kept pace with tech advances. Experienced and talented data scientists are scarce for love or money.
Businesses could overcome these challenges by:
- Relying on “as-a-service” vendors. These vendors make AI technology more accessible and affordable.
- Entrusting BI teams with AI responsibilities. Business intelligence knows the ins and outs of the business, especially what is important to the stakeholders. They can identify relevant data better than anyone in the enterprise can. BI teams do not have the expertise or the experience of data scientists. But technological innovations could bridge the knowledge gaps.
- Training BI teams in deep statistical analysis to make them competent in predictive modeling.
Implementing AI entails change, but adding AI capabilities to existing BI tools improves the potency and output of these tools, with users having to go through minimal changes or disruptions. Businesses save the cost and hassles of ground-up AI implementation.
2. Issue of focus
Many enterprises make the mistake of deploying technology for its own sake. They get blinded by the hype and follow the herd in installing the technology, with little thought about how to use it. Likewise, many data scientists remain obsessed with process accuracy. They focus more on research and model accuracy than on specific business results. They miss the woods for the trees.
For best results,
- Make sure the enterprise data strategy aligns with the business objectives. In successful companies, strategy leads the technology. Technology never leads strategy!
- Frame key business questions (KBQs) upfront. Make sure the KBQs align with the company’s strategic objectives. Consider the business strategy to increase market share through price reduction. BI answers, “How do price fluctuations affect sales over time?” But it does not answer the question of how to price the products to beat the competition. Predictive AI answers the question, “How to price products to increase sales while still making a profit?”
- Start with the end in mind. Focus on the business objectives and develop a blueprint to enable the model. For instance, if a customer has not purchased in a while, the aim could be to reward them to make a purchase. The AI-based BI model could help the marketer find the right combination of incentive and channel. For instance, a predictive model-based scoring system identifies customers who respond to discounts.
- Use BI and AI as relevant. Do not go overboard with AI because it is a new, more powerful, and advanced tool. For instance, if the strategic business goal is to achieve growth in monthly active users, use BI to track the number of active monthly users. Track if the number trends up or down compared to the previous months. Such insights make explicit the effectiveness of campaigns and customer retention programs. Machine learning (ML) analytics takes a proactive approach to increase the customer base. The ML algorithms predict long-term regular customers with high lifetime value.
- Enrich internal data with external data. The core of predictive analytics is internal transactions and customer data. External data sources, such as weather, public health, and holiday data, often enrich such internal data. These insights often throw up interesting correlations. To make the data model more accurate, automate the enrichment of internal data with relevant external data.
- Automate retraining Machine Learning models. Machine learning models become better over time, but not on their own. Left unattended, the machine learning models falter with time. It does not respond to changes in business strategies, customer preferences, and circumstances. Many companies deploy dedicated teams for ongoing model management. They also deploy automated solutions to monitor and retrain models, and ensure relevance.
3. The need for speed
Predictive and prescriptive models are hard to deploy. Most projects never go beyond proof-of-concept into production. Even when the projects do so, a new AI-based data project requires validation and pre-processing, which runs into weeks. Such time delays often make it unviable for businesses to run these projects. In today’s fast-changing business world, the model would have become obsolete by the time the data project becomes ready. To overcome lengthy delays:
- Use pre-existing BI-ready data and run analytics on it.
- Do not waste time seeking “perfect” data. Perfection is the enemy of getting things done.
- Deploy solutions that automate time-consuming data preparation and create AI-ready data sets fast. Such tools save months of data pre-processing.
- Validate prediction accuracy with A/B tests. A/B tests give fast and reliable results with a small sample size. After developing a model, test the results against a control group to see how well the model integrates with business processes.
AI enables the development of more innovative and more adaptive BI platforms. Businesses that use AI and BI in tandem gain a better understanding of their customers and improve process efficiency. They enjoy the benefits of synergy to gain a substantial first mover and competitive advantage.