Though we associate early business intelligence (BI) with reporting, it’s never been about simply displaying figures and numbers. Humans have always analyzed information to make deductions and decisions. It’s what makes us superior to other species.

Where does data analytics fit when seeking BI software, and how will it impact your buying decision? This article includes these and other frequently asked questions about business intelligence and data analytics.

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What This Article Covers

• What Is BI?
• What Is Data Analytics?
• Techniques
• Comparison
• BI For Businesses
• BI and Other Technologies
• FAQs
• Wrapping Up

What Is BI?

Business intelligence is a discipline that involves the tools and technologies for tracking how your business performs. Also, it refers to the data your business generates and uses.

Is all information business intelligence?

Not exactly. When your company shifts offices or how many floors you own isn’t business intelligence unless it feeds into your accounting systems.

Business intelligence should be

• Timely
• Accurate
• High-value
• Actionable

Business intelligence delivered at the right time gives decision-makers enough time to decide on the following action. Many industries rely on time-sensitive intelligence, such as stock markets, healthcare and transportation.

Business intelligence should be accurate and high-value, else it’s just information. So, you started your company 15 years ago? Hm, it might not be high-value information today unless you plan to sell.

Generating a report on the sales figures from five years back isn’t helpful unless you derive value from it.

Business intelligence must be actionable. You should be able to take the information and move forward with it to improve business. Else, what’s the use?

Maybe some products didn’t sell well in the past and are white elephants. Your BI insights will tell which are the laggards. It might be time to deprioritize them.

Are you planning to ramp up production? Your project management data will indicate if you have the resources or it’s time to hire new talent.

These insights take you from conclusion to action — hence the term actionable insight.

• BI drives customer segmentation that can guide you in designing personalized campaigns.
• Additionally, you can keep customers engaged with well-timed emails and in-app notifications.
• It involves tracking sales by recording the number and types of items sold. It also involves monitoring your buyers’ behavior.

What did they buy, and how often? How long after they added the item to the cart? Did they abandon the purchase midway?

Such information can help you identify gaps in service. Maybe your app is clunky?

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What Is Data Analytics?

Data analytics is the practice of exploring and analyzing data to draw conclusions for moving forward. Data analytics extends beyond business to science, agriculture, education and healthcare.

Is data analytics the same as BI? It’s a common question data science students and software buyers ask.

No, they’re not, though both deal with deriving helpful information.

• Data analytics is one of the tools for BI.
• Data analytics or big data analytics is the foundation of business intelligence. Big data analytics involves processing large data volumes, including real-time data ingestion.
• When data analytics serves business, we call it business analytics.
• When we talk of BI, we consider data visualization as part of it by default. But, dashboards and reports aren’t essential for data analytics, though analyzing big data volumes might be challenging.

While BI is about reports and presentations, data analytics is what happens under the hood. It’s complicated and not always simple, but it gets the job done.

Stitch co-founder and Talend SVP Jake Stein explains:

“Data analytics is about iteratively asking questions. The answer to any given question is often viewed only once and used to inform the next question on our way to answering a fundamental business question or solving a problem.”

Techniques

Descriptive, predictive and prescriptive analytics cover several data analytics techniques.

Descriptive analytics uses current and historical information to identify trends and data relationships. Every BI tool has descriptive analytics capabilities. Think tabular reports and visual data displays.

Viewing related metrics in individual widgets on one screen provides a comprehensive picture. It’s why dashboards and data visualizations are popular.

Look at this Tableau visualization and infographic of Finding Oases In Food Deserts.

It shows that areas lacking healthful food show the prevalence of high obesity and diabetes. The same regions have a more significant number of fast-food outlets.

Could it be the reason for the prevalence of lifestyle diseases in these areas?

Predictive analytics is business intelligence with data analytics at the backend. If you feel we’re mixing up the two again, here’s a reminder — there’s no escaping the BI-data analytics overlap!

Forecasting involves predicting performance and market trends using existing information, including past data.

Every department in your organization gains from data-driven foresight.

Sales, expense and revenue projections help forecast cash flow trends. Predicting product demand can assist in supply chain and procurement management.

Prescriptive analytics takes you from diagnosis to solution. Identifying improvement areas or revenue-generating opportunities is the precursor to action.

What should you do next? Instead of relying on guesswork, get suggestions on next steps from data analytics.

Beyond artificial intelligence (AI), machine learning algorithms self-learn to recommend what to do next. Self-driving cars and chatbots adapt and change their responses according to the stimulus.

Other data analytics techniques include the following.

• Statistical analysis
• Linear regression
• Classification
• Resampling methods
• Subset selection
• Regression analysis
• Monte Carlo simulation
• Cohort analysis
• Cluster analysis
• Time-series analysis
• Sentiment analysis
• Text analysis

Distributed processing technologies like Apache Hadoop and Spark enable fast performance. Besides, cloud computing brings advanced analytics within the reach of all enterprises.

Read our Descriptive vs. Predictive vs. Prescriptive Analytics article for a detailed comparison.

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Comparison

With their overlap, a business intelligence vs. data analytics comparison isn’t straightforward, though interesting.

To reiterate, BI is about describing what happened. Data analytics tells us why and how it happened and what we can do about it.

Business Intelligence	Data Analytics
BI supports decision-making using actionable insight.	Data analytics is the process of deriving actionable insight.
BI involves business reporting.	Data analytics is the targeted investigation and exploration of information.
BI involves analyzing past and current data.	It covers the past, present and future, including predictions.
BI techniques use structured data.	This methodology uses raw and structured information.
Basic data visualization and reporting skills are enough to create BI reports.	It requires expertise in algorithms, modeling, simulations and quantitative analysis.
Management and non-technical users rely on BI to derive insight.	Data analytics is limited to programmers, analysts and data scientists.

BI for Businesses

Every decision, whether big or small, strategic or tactical, impacts business growth. Business intelligence equips you with the necessary decision-making tools, including reports, metrics and outliers.

BI tools make the necessary data available per schedule and on demand. BI is often synonymous with reporting. Setting parameters to measure success and tracking them reveals how far you’ve come.

BI combines business analytics, data mining and data visualization. Additionally, it includes

• Real-time monitoring of business processes and metrics
• Performance management
• Dashboarding and reporting
• Benchmarking
• Data analytics
• Embedded BI

Sadly, many organizations rely on guesstimates when deciding the next course of action. Small organizations don’t have the budget, while large enterprises lack the oversight to enforce BI best practices.

“We’ve always done it like this” is a common explanation for decisions made without hardcore data to back them up. Complacency is the enemy of success.

A BI implementation requires a mindset shift and doesn’t end with acquiring a new software solution. Implementing BI is a best-practice approach to.

• Collecting data
• Making data-based decisions
• Assessing success based on key performance indicators (KPIs)
• Building on the learnings and carrying them over to subsequent decisions

It’s a continuous data gathering, analysis and reporting cycle.

Taking actions based on the available data, recording the outcomes and applying the learning to the situations going forward is enterprise BI.

The BI and data analytics overlap becomes a point of discussion when studying data science or buying software. As discussed above, data analytics spans the information landscape, and BI is one part of it.

Alternatively, data analytics is a component of business intelligence. The analytical functions and capabilities may vary by platform when seeking BI software.

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BI and Other Technologies

A BI platform is one piece of your tech stack, and how it works with other business-critical software matters. Being aware of these correlations is essential if you’re considering a BI implementation.

We look at BI with three technologies — data warehousing, customer relationship management (CRM) and enterprise resource planning (ERP).

BI and Data Warehousing

Before there were data warehouses, we had siloed sources of information, each with its format and structure. Gathering information from them was an uphill task.

Soon enterprises sought a centralized information repository to organize data in a standard format and push data on demand, making retrieval faster and easier.

At the same time, the demand for quick data access was gaining ground. Combining business intelligence and data warehouses was the natural next step.

• Data warehouses are read-only and provide accurate, timely and high-value insights.
• Advanced storage techniques like metadata referencing in warehousing solutions give you fast information access.
• Routing all information into one store and extracting it with BI tools speeds up the time to insight.

BI and ERP

Enterprise resource planning covers finance, human resources, project-based ERP, order management and accounting.

• ERP systems came into their own in the early 2000s at the turn of the century, filling in a gap earlier enterprise systems couldn’t fill due to the Y2K bug.
• Over the years, demand for ERP software with built-in BI and reporting increased.
• Many ERP software solutions offer robust BI and analytics capabilities.

It resulted in greater market penetration of BI tech with the result that every organization has some semblance of business intelligence working for them.

Software buyers have more options to choose from, including tailor-made solutions.

SAP Business One is a BI, reporting and advanced analytics software suite serving the product lifecycle from production to sales.

BI and CRM

CRM platforms are software programs that gather and manage customer data.
Customer preferences drive business operations. CRM data feeds into procurement, inventory, accounting and finance systems, overlapping with ERP.

• It includes information on lead and buyer interactions with your services and products.
• Tracking conversion trends and managing client relationships is essential to sustained business growth.
• Initially a reporting service, CRM evolved to include customer service portals and applications. It keeps you in touch with buyers after the sale to get product feedback.
• User reviews help you improve your offerings and tap into the market’s pulse. It builds healthy customer relationships, establishing your reputation as a serious player.

Many CRM platforms have BI capabilities that may include predictive and prescriptive analytics, so you’ll need to check with the vendor.

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FAQs

Who are the users of business intelligence?

BI supports investigation into metrics and decision-making in all departments and organizational levels.

CEOs and CFOs rely on high-level, interactive reports for approving strategies and budgets. They might not have the skills and the time to create dashboards, so ready insights help.

Managers and executives may or may not have report generation and analytic skills. It’s why CEOs prefer software with prebuilt dashboards and reports. It saves time and speeds up decisions.

Data analysts are experts in data exploration and manipulation. They know which techniques to use to get the necessary information.

IT experts are professionals who manage the BI system and track its health. They track software adoption with usage and performance reports.

Report consumers include top management and external users, including clients. They may have limited permissions to interact with or change the data in the reports.

What are some examples of business intelligence tools?

Power BI, Oracle Analytics, MicroStrategy, TIBCO Spotfire and Qlik Sense are examples of leading BI tools. Refer to our product directory for more information.

What are some examples of data analytics tools?

Hadoop, Tableau, Domo and Cloudera are popular data analytics platforms.

Which business needs does BI address?

• It enables benchmarking to keep you on par with other players in the market.
• Business intelligence provides data management and improves data quality. It helps improve your bottom line by revealing improvement areas and highlighting successes.
• It boosts productivity with accurate and reliable data.
• BI speeds up reporting for proactive decision support.
• It helps improve customer satisfaction by driving better product quality. Team collaboration and real-time updates help resolve issues faster.

Which business needs does data analytics address?

• You can forecast trends by revealing patterns and data correlations.
• It helps boost efficiencies by identifying gaps and blockers that hamper performance.
• It gives helpful insight into customer behavior and market trends.
• Data analytics supports product development by predicting demand based on buyer trends.
• Risk management becomes easier with predictive analytics. Planning in advance helps you get back on your feet faster in the case of losses.

Shall I opt for a BI or data analytics solution?

Many BI tools have data analytics capabilities and vice-versa. Assess your business needs internally before framing questions to ask potential vendors.

Your existing infrastructure and tech stack will determine what you need. An upgrade or add-on might suffice if you want advanced analytics on top of your BI tool.

The same goes for data analytics tools that lack essential BI features like reporting.

But consider acquiring a new tool if the integration looks clunky or if an additional BI tool will work better for you, provided it’s within your budget.

Software vendors provide custom ERP, CRM or analytics-based BI tools, and with the wide variety available, one is sure to match your needs.

So, what are you waiting for? Call us to put you in touch with leading software vendors.

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Next Steps

Keep your organizational needs front and center while seeking a business intelligence and data analytics solution.
Interactivity, self-service, data visualization, advanced analytics and reporting are some features to consider in potential tools.

Need help? Get started with our free, customizable requirements template to easily set your must-have features.

From there, you can compare your shortlisted BI and analytics tools by generating feature-by-feature comparison scorecards.

How did business intelligence and data analytics make a difference to your business? Let us know in the comments.