Business Intelligence Data Analytics

Can Decision Intelligence Drive Your Analytics Strategy?

The earliest forms of decision intelligence emerged around 2012. Since then, decision intelligence technology has gained traction in data science and product management fields. Ultimately, this type of technology has quite a lot to offer in many professional organizations, given the monumental amount of data we have at our disposal. We’ve gathered all the information you need to help you understand what decision intelligence is and how it can help business professionals streamline their workflow across multiple industries. 

What Is Decision Intelligence?

Before we jump into how organizations use decision intelligence as a part of their daily work, you need to understand the fundamentals of decision intelligence. At its core, decision intelligence focuses on utilizing machine learning and data analytics to help professionals make important business decisions. Decisions, in this instance, often consist of irrevocable resource allocation or strategic actions that have undeniable and irreversible consequences. Therefore, when a stakeholder is responsible for making a decision, ensuring they make the correct definitive decision is essential. 

Decision intelligence takes advantage of the availability of machine learning and an abundance of available data to analyze circumstances, find patterns and predict outcomes. While many individuals may claim that data scientists can do just that, there’s one key difference: the AI utilized in decision intelligence operations focuses on the facts, statistics, patterns, and expected outcomes. 

Finally, it’s important to note that there are many different forms of decision intelligence techniques, including but not limited to: 

  • Decision management
  • Agent-based decision systems
  • Descriptive analysis
  • Decision support
  • Diagnostic and predictive analysis

Ultimately, decision intelligence exists to help stakeholders understand the potential outcomes of key decisions made during various project stages. 

Why Is Intelligence Analysis Important?

Decision intelligence has continued to grow over the last decade and will continue to develop. Industry experts believe that these tools will be available in regular consumer software suites like Microsoft Office in the years to come. Therefore, it becomes obvious that there’s a need for these tools, but why? 

To put it simply, one of the biggest problems with human decision-making involves the inability to see real-world results from all angles. For instance, decision intelligence empowers businesses to automate some parts of the decision-making process using machine learning and data-driven observations. 

When a business takes advantage of intelligent analysis and decision-making, they’re likely to experience many benefits. Some of these benefits include: 

  • Faster response time to disruptions
  • More accurate decision-making
  • Developed framework for long-term effects of immediate decisive action
  • Reduced risk as a result of poor decision-making
  • Improved ROI on many projects as a result of faster turn-around times

These are just a few of the benefits that decision intelligence offers. Managers of organizations looking to optimize and improve their workflow need to take advantage of decision intelligence to reduce project risks effectively. 

How Does Analytics Play A Role?

Analytics is one of the primary aspects of utilizing decision intelligence, AI, and artificial decision-making. Data scientists often review data and draw logical conclusions based on the data at hand. However, decision intelligence can take this process to the next step. 

Many organizations already store and utilize a large amount of data on internal servers. Decision intelligence streamlines the process of reviewing, analyzing, and drawing conclusions from the data gathered, presenting logical conclusions based on the data provided. 

Because so many organizations already store data, much of which goes unused for large periods, decision intelligence has many applications. Organizations can streamline the analytical process and spend more time seeing the results of improved decision-making abilities.


Overall, many businesses now rely on decision intelligence frameworks for automating the decision-making process. By understanding the effects of artificial intelligence, intelligent analysis, and logical decision-making, stakeholders can take advantage of machine learning to directly improve their workflows. While the concept of decision intelligence may still be relatively new, it has already been shown to provide businesses with the power they need to overcome obstacles and make effective decisions on a consistent basis.

Business Intelligence Data Analytics

8 Questions You Need to Ask Before Building a Data Warehouse

Does your business need a data warehouse? On one hand, they accommodate complex modeling, improve workflows, and ultimately increase profits. On the other hand, data warehouses can be resource-intensive and expensive, rendering them impractical in certain situations. 

1. How Long Have You Been Collecting Data?

Are you sitting on a pile of customer data? If not, you might not be ready for a warehouse just yet. Startups may find that capital is better spent securing personnel, building infrastructure, branding, and marketing. However, if you’re sitting on mounds of information ripe for analyzing, investing in data warehousing could be the game-changer to your bottom line.

2. Do You Have a Lot of Reporting Systems?

Is your current reporting environment a patchwork of different systems held together by a fraying thread? If so, why not streamline and house everything under a single data warehousing umbrella. Not only will it improve workflow, but you’ll be better able to investigate historical data and compare it to recent trends.

3. Do You Have Custom Reporting Systems?

Custom reporting is the backbone of many businesses. If your company relies on specialized reports created in the corporate dark ages, it’s probably time to update, and moving to a data warehousing model may be ideal. Querying becomes infinitely easier with a centralized system as opposed to a siloed setup.

4. In What Formats Is Your Data Stored?

Data comes in a variety of forms. If over the years, you’ve switched management systems, legacy data may be in formats that no longer work with your current setup. By investing in a data warehousing system, you’ll be able to create a digital ecosystem that accommodates multiple formats, which are normalized at the extract, transform, and load — aka ETL — stage.

5. Are Your Modeling Efforts Complex?

The more complex data and reporting needs are, the more helpful a data warehouse can be. Plus, having a streamlined system may illuminate new metrics that can be studied and leveraged.

6. Are You Frustrated by Reporting Performance Issues?

When reporting against operational systems, data can become volatile. Information sets can morph into forms, like a substance changing from ice to gas. When it happens, your reporting can become filled with errors. However, data warehousing mechanisms, which are typically optimized for read-access only, often eliminate the querying and processing kinks, resulting in fewer output headaches.

7. Do You Need to Perform Multi-Year Data Transformations?

Businesses with multi-generational data frequently benefit from a data warehouse. Insightful and profitable realizations can be mined with a powerful querying system.

8. What Resources Are at Your Disposal?

Though often worth it in the long run, developing a data warehousing system can be a costly and extensive process, especially if you attempt to build one without the help of advanced tools. The return on investment isn’t always immediately evident. So if the money isn’t readily available, it may not be time. After all, a poorly executed data warehouse can waste time and productivity. Wait till you have the resources to get it done correctly or enlist the help of AI-powered tools.

Business Intelligence Data Visualization

What is Data Storytelling?

What is Data Storytelling?

How do you tell a GREAT story with data? 

Everyone likes hearing a good story. However, being asked to “tell a story” using data and visualization is often a big source of anxiety for analysts of all backgrounds.

An informal Twitter poll returned the following responses to the question “When I’m asked to show the data, I feel….”

Frustrated, because I don’t think I’ll tell the story effectively, and might miss important parts.

I feel pressure, pressured to make it clear for everyone, and what if people don’t like my story?

Inadequate, because I’m sure there are questions people will have that I haven’t anticipated.

Being able to tell stories with data is a skill that’s becoming ever more important in our world of increasing data and the desire for data‐driven decision-making. As more and more data visualizations are produced, they start to become a commodity and their quality suffers. This turns off viewers and people begin to rethink their investments. However, great Visual Storytelling can send the effectiveness and reach of your analysis through the roof and produce significant business influence, value, and career rewards.

Have a look at the graphic below. What story do you get from it? 


Here’s what most people see:

  1. The average surface temperature is trending higher.
  2. Multiple independent data sources all show the same trend, which lends further credibility.
  3. Temps were trending lower at one point, but that reversed and has started growing in lockstep with the rise of industrialization.

Even though it’s little more than a few words, numbers, and some colored lines, it tells a very compelling story, with strong supporting evidence, and makes its key points very persuasive.

Storytelling with data is no different than regular storytelling. Storytelling is by far the longest-running and most effective method of human-to-human knowledge transfer. The reason storytelling is so effective is that it engages emotions along with cognition (ability to learn). Emotion keeps you interested while you learn. 

  • Build Characters: First and foremost, stories involve characters. Without humanized characters, there’s nothing for the viewer to relate to; It’s not a story. Think about it. Every story you’ve ever read has human-like characters for you to relate to. Even movies and stories about animals and inanimate objects impart human characteristics to those characters: they talk, they react, they have expressions and emotions, they act human. In data, the character(s) can be you, or the reader, or named people, or people in a certain role, customers, employees. e.g. “Our Sales managers wanted to know how to ….” “I sought to uncover why ….” “Our CEO, Mike, asked me to investigate why ….” or even, “My daughter asked me about our company, and she wondered why X product was so successful….” A great way to introduce characters visually is with earlier Survey Results. The survey results let you introduce and describe characters, “Male employees, under 40, working in our US Offices had the following to say in a recent survey:” and also give them a voice.
  • “Use the Force, Luke”. The next thing that is required is some kind of goal, challenge, or objective. This line from Star Wars was Obi-Wan’s way of challenging Luke to learn the Jedi way and set out his hero’s journey. The characters must want or need to do something. That’s the hook. That’s what gets the reader to go along with the character, to put themselves in their shoes. In data, a great one here is answering a difficult question or solving a business problem. Even better would be showing how your insights and answers resulted in a measurable improvement. So give your characters a challenge to overcome. The bigger the challenge, the more interesting the story becomes because….. 
  • ….“Never Tell Me The Odds”: Give the character some stakes, some consequences of complete failure. Optional: Let the character fail, but use it to illustrate what they learned. Failure makes the stakes seem real and pulls the reader in even further. This is called “rising action”. Just don’t lay it on too thick.
  • Have a Point!“: Every story needs a climax, a point where the action peaks. This should involve being the main message you’re trying to communicate. It should always involve your character(s) achieving or exceeding their goal. For example, if your “challenge” was to use analysis in order to create a plan to change something, you can include a Gantt chart or Change Roadmap here as the deliverable of that analytics journey. You could also phase shift this slightly to show a visualization of what the Outcome of that Change was.
  • “Falling Action.” Now that you’ve made your main point, you can use this part to tie up loose ends, resolve other challenges or conflicts besides the main one, describe what happened to the characters after the story, or even use it to tease a sequel. You can also use this part as a “Call to Action” for the viewer if you want them to do something, such as give feedback or share your dashboard with others. 

Now that you have the main foundations of Data Storytelling under your belt, give it a try. The best way to master it is to practice it often. Learn what works best for your data and your audience. A-B test different approaches and get feedback on what worked. Look back over earlier work from yourself and others and list out the things you might have done differently now that you have this new knowledge. Hopefully having a structure like this to start with will give you confidence in choosing and arranging your next visual exercise to maximize its message and persuasiveness. How will you know you’ve succeeded? People will tell you. People know good storytelling when they experience it. Good luck!

Business Intelligence Data Visualization

3 Powerful Steps to Data Storytelling

In the digital economy, data is mana. It’s the fuel that keeps the tech and marketing sectors churning. But when using data as a sales or education tool, plain old stats and facts just aren’t enough. Massaging data into a compelling story is key to onboarding clients, securing investors, and training employees.

Human brains are wired for stories. As evidence, a Stanford Business School study revealed that 62% of participants remembered stories while only five percent remembered straight statistics. That’s an immense difference, and the results should have every business asking: How can we transform our data into engaging narratives that sell, convince, and teach?

What is Data Storytelling?

Without framing, data can come across as flat, bland, and vulnerable to interpretation. Businesses wanting to burnish their brands in the brains of target audiences must carefully craft their messaging and bolster it with supporting data.

Why is Storytelling So Effective?

Storytelling is how cave people evolved into modern individuals. It’s a linguistic tradition hard coded into our DNA; it’s how civilizations passed down survival skills and traditions. Storytelling remains an integral part of how we process and retain information.

Read more: Data Storytelling: The Essential Skill for the Future of Analytics

What is the Goal of Storytelling?

The goal of data storytelling is to engage audiences. You can highlight insights that will stick, convince, and stimulate the desired action by packaging narratives in digestible and engaging bites of information.

As a presenter, your job is to focus people’s attention on the most salient and engaging points. Think of yourself as the Degas of data — someone who paints beautiful pictures using stats and trends. By framing the mundane in gilded casings, you’re heightening the audience’s emotional response, which leads to better retention of the material.

Three Steps of Effective Data Storytelling

We’ve discussed why data storytelling works. Now let’s dig into the “how” of the matter.

Become Intimate With the Data

Before crafting data stories, familiarize yourself with the information. Don’t manipulate the data to suit your needs. People instinctively pick up on phony or inflated stats — and that diminishes trust. Instead, become intimate with the facts and figures and find the actual statistical trends hidden within. They’re more impactful than jerry-rigged half-truths.

Understand Your Audience

The next step is getting to know your audience. What makes them tick? What do they care about? What’s their worldview? How does your data connect to their goals? The answers to these questions will shape a story that connects with your targets emotionally. Once you tap into their zeitgeists, you can more readily sell the vision.

Remember that one size does not fit all when it comes to data narratives. The tale you tell to a room full of mid-level managers will differ from the one you tell executives.

Choosing the Right Data and Presentation Style

Visuals matter — a lot. They help clarify, connect, compare, and provide context. Effective visualizations include information about the most compelling data as well as highlight the best parts. While it’s almost always better to have professionals design presentations, here are a few DIY pointers:

  • Comparisons: If you want to highlight comparisons, use bar, line, and circular charts.
  • Composition Statistics: Showing data composition statistics is best done with pie charts.
  • Distribution of Data: Line distribution charts work for displaying data distribution points and trends.
  • KISS: When creating charts and graphs, adhere to the adage “keep it simple, silly.” Leave the 3D renderings and drop shadows to game developers. They only mess up business presentation aesthetics and may come across as outdated.
  • Color Consciousness: Data presentations are not the time to express your inner Rainbow Brite! Choose a pleasant color palette and use complementary colors; they’re easier to understand at a glance than a hodge-podge of hues.
  • Language: Use words and phrases that your audience understands. Don’t try to “sound smart.” It never works and can reflect a lack of confidence and communication.
  • Layout: Each slide should have a call to action, a header, and a short narrative summary. People’s attention wanders during presentations. Combat this by keeping things clear and concise!

Crafting effective data narratives that speak to people’s desires and emotions is a skill that takes time to develop. Professionals understand how to mold micro and macro elements into engaging stories. It’s no question that data storytelling is an invaluable tool. And when done correctly, profits follow.

Big Data Business Intelligence Data Analytics

How to Drive Growth & ROI with Marketing Analytics

What is Marketing Analytics?

Analytics, at its core, is the study of numbers. Numbers provide valuable data and insight into any given marketing strategy by making performance quantifiable. With marketing analytics specifically, the numbers help to track, identify, and understand user behavior. 

Understanding your audience is critical to making sound marketing decisions that deliver the best ROI and drive positive growth. 

In a simplistic breakdown, marketing analytics are intended to perform in two ways: 

1. Measure the effectiveness of marketing strategies and campaigns. 

2. Identify opportunities for improvement that will yield greater results. 

Why Do Marketing Analytics Matter?

Without marketing analytics, a business is operating blind. The marketing campaigns are merely pushed out to the world with little to no understanding of how strategies are landing with your audience. 

 Marketing analytics matter because: 

  • It makes the actions quantifiable. Whenever numbers are used, it provides concrete data for the marketing program. For example, it’s easy to notice that overall sales increased after a personalized content marketing campaign. Though, a more effective approach would be tracking the specific percentage (25% e.g.) of traffic that came from a blog published at a specific time (November, e.g.) and converted a specific number of leads (5% e.g.) for a specific product (holiday gift, e.g.).
  • It helps plan for future marketing. When you understand which tactics are working, you can strategically plan for future marketing. Not only does this help with planning marketing activities, but also overall budget allocation. 
  • It identifies the “why” of what did or didn’t work. After a marketing campaign or strategy has launched, the only way to adequately understand performance is with marketing analytics. The data can be drilled down to track individual messaging across a broad spectrum of outlets, ensuring no approach is wasted. For example, maybe your click-through rates from social media to your website are fantastic, but not converting to sales. With this information, you can focus your energy on shifting the homepage to reduce bounce backs.

How Can Marketing Analytics Drive Growth?

Ultimately, the information provided by marketing analytics is meant to drive growth and provide positive ROI. There are a few key ways that marketing analytics can help drive growth: 

Identify target audiences. One of the most brilliant things that marketing analytics can do for businesses is to segment audiences. The analytics can help identify and group users by:

  • Age
  • Gender
  • Geographic Location
  • Income Level

Even further, marketing analytics can identify subgroups or intersections in data sets. For example, a segment could be women, aged 30-45, in Tulsa, OK.

When grouping users together, data can be extracted surrounding how to best target those groups. Marketing analytics can also reveal new groups that are worth targeting. For example, perhaps the target audience is believed to be women aged 30-45 years old, but you find that certain marketing tactics are delivering positive results in teenage boys aged 16-20 years old. Having that information is powerful when driving growth. 

Predict future user trends. Predictive analytics compile past trends and historical data to help determine how users will behave in the future. This can help plan marketing strategies to align with certain seasonal behaviors. For example, sales of certain products are higher during the summer. By targeting audiences in the discovery phase in spring yields the most beneficial results. 

Eliminate what doesn’t work. One of the best ways to reap rewards with marketing is to eliminate the tactics and strategies that aren’t working. The less time and money spent on fruitless endeavors, the more growth can happen. 

Which Marketing Analytics Deliver the Best ROI?

The answer to this question will be different for each business. However, some general items will ensure marketing analytics deliver the best ROI. 

1. High-Quality Data

The power is in the numbers. Data that is quality both in scope and extraction is so important to delivering the best marketing ROI. The best quality data will be: 

  • Current
  • Consistent 
  • Precise 
  • Accurate
  • Relevant 

2. Combine Past, Present, and Future Data

To achieve a comprehensive overview of marketing analytics, all data should be considered. The past provides insight into user behavior and trends, while the present focuses on relevant current climates. The future is a prediction based on past and present trends, therefore eyes must also be turned toward the future to properly steer marketing strategies.

Business Intelligence Data Analytics

How to Learn Data Analytics with the Feynman Technique

Using the Feynman Technique to Enhance Your Data Analysis 

The field of data analysis is an ever-growing, ever-changing industry. Most data analysis advice for best practices will go into the technical needs for the field, such as learning specific coding languages and relevant algorithms. However, to fully grasp your data analysis, you must be able to make it easy to comprehend for people outside of the field, such as business users or the general public. Thankfully, there are positive qualitative techniques that you can employ in your analytics practice to help with this, particularly the methodology known as the Feynman Technique.

Why is the Process Called the Feynman Technique?

The Feynman technique is named after the world-renowned theoretical physicist, Dr. Richard Feynman.

Who is Richard Feynman?

Dr. Feynman was a Nobel Prize-winning scientist, university professor, and writer. He was best known for both his work in the field of quantum electrodynamics and his involvement in major historical scientific events, specifically his work on the Manhattan Project and his official investigation into the Challenger shuttle explosion. As an educator, he was best known for his approach to teaching, which emphasized true understanding of the subject matter, as opposed to the then-standard of conventional learning techniques.

How Does the Feynman Technique Work?

The Feynman Technique is a multi-use means of understanding any new data, regardless of the context. The general goal is to better understand the information by effectively explaining it to others. The technique works by adapting Feynman’s personal approach to understanding data and involves a small number of steps to achieve this process. 

1. Study the Data Thoroughly

In order to fully understand a set of data, Feynman believed that you had to first truly study everything about it. In many cases, there are numerous items in a data set that might need additional study to thoroughly understand the data set as a whole. In these cases, the Feynman Technique dictates that you should narrow your focus to those items you might have any difficulty focusing on first.

2. Explain the Data

As an educator, Feynman believed that the next step for data, once understood, was the ability to teach it to someone else. For this step of the Feynman Technique, once a data set is truly understood, you then teach what you have learned to another person or group. It is at this stage where you welcome questions and feedback, this allows you to spot any weaknesses in your analysis or overall understanding of the data.

Further Study

If there are any gaps or inconsistencies that your audience points out in Step 2, this is where you return to the initial data set and dive deeper into those areas. Ideally, the more these points are analyzed, the more they will become the strongest points of your overall knowledge.

Create a Simplified Explanation of the Data

Once you have a thorough and reasonably airtight knowledge of the data and its implications, the last step of the Feynman Technique is to break down your analysis into as simple and basic an explanation as possible. This enables the fastest and most efficient means of communicating to your clients, coworkers, or any other audience you might have. From time to time, you will have to go into further details when asked about specific points related to your analysis, but for most audiences, basic information works best to allow others to understand it quickly.


In today’s modern society, secondary and higher education now emphasizes project-based learning and a more thorough understanding of the subject matter. With up-and-coming analysts approaching data with the Feynman Technique, or a similar model, this strategy enriches the overall quality of your analyses, and will most likely benefit you throughout your career.


Big Data Business Intelligence Data Analytics

Top 5 Growing Data and Analytics Trends for 2021

What Are the Top 5 Growing Data and Analytics Trends for 2021?

Today, data and analytics are constantly evolving the way we do business. Companies are becoming heavily reliant on historical big data to create a foundation for decisions, from driving cost efficiency to determining new strategic directions. These decisions are more important than ever, especially when businesses are battling to stay competitive amongst the digital frontier.

To keep the competitive edge in the turmoil of a global economic crisis, more companies are taking the lead to be proactive in data analytics. The traditional AI techniques of being driven by big data are rapidly transitioning to the class of analytics based upon smaller yet more varied data techniques. Automation technology is the solution to this rapidly growing, complex problem.

Here are five of the biggest analytics trends to look out for in 2021.

1. Why Analytic Automation with Advanced and Scalable AI

With the demise of “big data” and the pivot to “small data” analytical techniques, AI systems will be required to do more with less. Advanced AI has the unique ability to analyze complex data sets and quickly detect trends and patterns that would be challenging or easily overlooked by the human eye. Analytics automation provides the opportunity for corporate analysts to focus more on high-value targets to drive top-line growth. Intelligent automation is the “engine” for today’s complex and challenging data-driven decision-making.

2. How Xops Delivers Automated Analytics for the Enterprise 

Xops has an objective to increase efficiencies with an economy of scale by deploying DevOps best practices. The end goal is to reduce cross duplication of technology automation processes while maintaining high levels of reliability, accuracy, and reusability. The key components of Xops (data, machine learning, model, platform) scale with DevOps best practices to maximize the value of analytical information. Xops promise to automate and accelerate the collection, collation, and identification of data elements, ultimately helping organizations keep their competitive edge.

3. What Dashboard Automation Promises for the Organization

The rapid movement to deploy data automation solutions that deliver insightful user-created data has tremendous implications. Analytical data traditionally would have to be delivered by IT or a data expert within the organization. The promise of analytical data generated on-demand by anyone ranging from marketing, human resources, or even finance will shift organizational agility, delivering insights faster and more effectively to the company as a whole. The impact on an organization from decentralized to on-demand data delivery can be dramatic.

4. Why Cloud Services Are Rapidly Growing for Analytical Data

With the advent of increasingly complex and larger data sets along with their intrinsic values, cloud services are rapidly becoming the repository of choice. Data is stored outside an organization on remote, secure servers. Extremely valuable information is better protected and in case of a disaster, data can be recovered much more efficiently. Scalability for the enterprise is more fluid with the cloud hosting services.

5. Why Data Analytics Has Become a Core Business

Data analytics has transitioned from being a secondary support function to mission-critical for an organization. The buy-in for data analytics has widespread support from the board room and the C-suite for its tremendous potential. The positioning of data analytics as a core business function, though, does not come without cost. Often, businesses may underestimate the complexity of data collection and analytics, missing valuable opportunities. Many times, partnering with AI-powered tools such as Inzata will immediately shorten the ramp-up time to deliver valuable data assets.


These data and analytics trends are being driven by analytics automation platforms. Technology is rapidly advancing, and organizations embracing it first will enjoy a competitive edge. Previously, a few gatekeepers generated the data analytics and insights based upon specific data requests from corporate analysts. With the trend for insight generation being pushed down to the individual, data analytics becomes available to all. The ability to create insights on-demand by the end-user will inevitably lead to a leap in corporate strategic planning, decision-making, and the ability to maintain a competitive edge.

Business Intelligence Data Analytics Data Visualization

What is a KPI Dashboard?

What is a KPI Dashboard?

Every day your business collects hundreds or even thousands of data points, and it can be overwhelming to wade through all of this information. Key Performance Indicators (KPI) are the different metrics used to access if you’re reaching your business objectives and goals. 

There are numerous KPIs that businesses can use, but the most common are Quantitative, Qualitative, Lagging, and Leading KPIs. 

Quantitative KPIs deal with numerical data such as currency, percentages, and statistics. Qualitative KPIs, on the other hand, takes into account the interactions of your customers. It factors their opinions, experiences, and even feelings towards your business or product. 

Lagging KPIs takes a look at historical data and uses it to predict future outcomes. Most Leading KPIs, though, look at performance for future forecasting. Typically these will be used together to help increase your overall visibility. 

Looking at all of this data separately can be both time-consuming and inefficient in the use of resources. A KPI dashboard can look at all of this data and provide you with key information at a glance. That allows you to monitor goal performance, find ways of improving workflows, and make sure that you’re making the best use of your resources. 

Who Can Benefit From a KPI Dashboard?

Businesses that are already gathering any form of operational or transactional data can benefit from a KPI dashboard. Organizations that need to make adjustments to their workflow or data collection processes would also benefit from a KPI dashboard. They can see information at a glance to know if the business is over or underperforming in regards to trends, quarterly goals, and business strategy. 

Companies that have numerous departments or organizational levels can take advantage of a KPI dashboard. Each business function may have its own individual goals and the dashboard can help paint a clearer picture of how that fits into the organization as a whole. That way you can manage your target strategies and identify which areas of your operations need to be streamlined. 

What Are Some of the Benefits of a KPI Dashboard?

A KPI dashboard can provide you with new insights into how your business is performing. Whether you want a high-level view or need to drill down for more detailed information, you can customize reporting to meet the needs of your business or department. Additionally, the KPI dashboard can also be customized to meet the needs of individual users. That way the data can be tailored to what’s relevant for each person’s role and daily responsibilities. 

Some examples of KPIs that you can put into your dashboard can include:

  • Revenue per customer
  • Project time
  • Churn rates
  • Net profit
  • Revenue growth

Depending on the size of your business, you could be using hundreds of KPIs. Regardless, dashboards will work to filter and highlight the key information. 

Another benefit of a KPI dashboard is that you can scale the data. You can get down to individual performance metrics, departments, or teams so that you can easily access desired information and find ways of improving performance. 

Using a KPI dashboard can also help businesses make decisions about their investments. You can get real-time updates to monitor the ROI and use historical data to forecast profitable future investments.

Why Should I Use a KPI Dashboard for My Business? 

When building a KPI dashboard, you should consider metrics that are relevant to the goals of your business. Examine the various stakeholders in the organization and access who will need to access the data, then choose the KPIs that align with their goals and strategies. 

When selecting individual KPIs, you should focus on actionable metrics. Be specific with your goals and focus on the ones that are of the highest priority. If the business strategy changes, you can always go back and adjust the KPIs to realign with your new approach. At the end of the day, the KPI dashboard that you use should help improve your business. 

As your goals and strategies change, the KPI dashboard should be robust enough to adapt as well. A KPI dashboard that works well for one industry may not work for yours, so it’s important to do your research and find the right components for your business.

Big Data Business Intelligence Data Analytics

The Real Competitive Advantage of Real-Time Analytics

Information is power, or so the saying goes. Yet, that power rests in large part on how recently that information was collected. Let’s say you want to buy a house, you take the tour and like what you see. Before you make your final decision, though, you want to see a home inspection report. Do you want to base your decision on a home inspection report from six months ago or the one the home inspector will finish an hour from now? How about an inspection report from one month ago? One week? 

Information is constantly changing, anything could have changed within the past few weeks alone. To make effective and accurate decisions, you need to be working with the most current information. That desire rests at the heart of real-time analytics and the competitive advantage it offers.

What is Real-Time Analytics?

Businesses are inundated with data from countless sources. They get data directly from customers, aggregate data from social media, traffic data from websites, and even from marketing tool suites. Real-time analytics takes all of that data, processes it, and provides up-to-the-moment results. 

The kind of results depends on the analytics software you use and the settings involved. For example, some businesses prefer that the software provide answers only when queried on something specific. Others prefer that the software offer real-time alerts or trigger real-time actions for certain pre-set results.

Why Does it Give You a Competitive Advantage?

No business can know with certainty what data its competitors possess. You also can’t know with certainty how soon they’ll analyze that data and put it to work. Leveraging that real-time information lets you adjust tactics, change orders, or even create promotions in response to new trends before your competitors do. 

That lets you collect on the immediate benefits of reduced waste, better marketing, and an uptick in revenue. It also helps solidify your business as being on top of what is happening in the world. Customers like businesses that either predict or seem in close tune with the market. Every time you seem on top of your game, you cement your business as the one people should turn to first. 

What is the Cost of Working with Outdated Data?

So, what are the pitfalls of working without outdated data? For some functions, such as forecasting, data that is a little out of date probably won’t change the results by a significant amount. 

For any business that must respond quickly to rapidly changing trends, the cost can prove to be high. Let’s say a trend highlighting the value in some otherwise obscure IT function is rapidly developing in a specific industry. If you run a managed IT service company, recognizing the trend quickly lets you update your service offerings, set pricing, and shift your targeting strategy. If you don’t catch that trend quickly, you can lose out on potential revenue from new customers and expanded services for old customers. 

Use Cases

One of the most obvious use cases for real-time analytics is monitoring your IT infrastructure. Immediately you are able to gain visibility into the capacity, availability, and performance of your infrastructure investments. This lets you respond to any issues as soon as they arise. Getting the information tomorrow won’t be any help to your service levels. 

Another common use case is for digital marketing efforts. Let’s say that you offer a rewards program for customers. When they are shopping online or in an app, it’s a golden opportunity for some personalized marketing based on their previous purchase history. Real-time analytics can alert you or your automated marketing system that the customer is browsing the store. That lets you deliver customized discounts, coupons, or personalized promotions when someone is most likely to buy.

Real-time analytics is a powerful tool for carving out a competitive advantage. It helps keep your company at the forefront of changing trends. It also helps your business adapt faster when the unexpected happens. In turn, you reap short-term as well as long-term benefits in terms of cost-savings, revenue boosts, and customer conversions.

Big Data Business Intelligence Data Analytics

What is the Half Life of Data?

Half-Life of Data Mean?

The term “half-life” was originally coined by scientists studying the amount of time it takes for at least 50% of a substance to undergo an extreme change. When studying analytics and data science, the term often comes up. 

While the half-life of data isn’t as exact a measure as the half-life of substances, the implications are similar. In this case, the half-life of data is referring to the amount of time it takes for the majority of it to become irrelevant. This is an exponential curve downwards, meaning that data is at its peak value when first collected, then accelerates in loss of value over time.

In a recent study, researchers highlighted the issue that administrators often underestimate or misunderstand the half-life of their data and the implications it carries. 

What Are the Three Business Categories of Data?

Nucleus Research found in their study that businesses driving decisions with data fall into one of three categories: tactical, operational, or strategic. The half-life of data varies by the business data category.

These categories were self-identified, and no real-world business is only one of these categories. Companies in the study were asked to select a category based on four factors: their suppliers, their markets, how regulated they are, and how much they depend on intellectual property.


According to the study, the tactical category contains companies who utilize data to influence their processes in almost real-time. Because data received is extremely valuable when first received, then rapidly declines in value to the company, this category has the steepest downward curve of data half-life.

This category emphasizes how important it is for companies to have technology that allows them to act as quickly as possible on actionable data. The study found that, on average, the half-life of data in this category is a mere 30 minutes. That means data is losing a majority of its value in the first 30 minutes after collection!


The study indicates that companies using data for operational purposes generally require it to make decisions that could be anywhere from a day to a week. This is a mid-level category with a half-life curve that goes down exponentially, but far more slowly than data of companies in the “tactical” category.

Nucleus Research found that data in this category had an average half-life of 8 hours but ranged widely among companies, from one hour to 48 hours.


Companies falling into this category use data for long-term processes and plans. Strategic data’s value is the most distributed, losing value very slowly over time. The half-life of their data is a small-slope linear graph. Strategic data’s average half-life is 56 hours and widely variable.

What Are 3 Ways to Speed Up Conversion from Raw Data into Actionable Insights?

Here are three ways to divert data from silos and process it into valuable and actionable insights for your business.

Ask Good Questions – In order for raw data to be valuable, it must have a defined purpose. Meeting with all stakeholders and determining what specific question you’d like answered, then identifying data that must be collected to answer it instantly increases the value of what you’re already collecting.

Use Segmentation – If possible, differentiate among types of clients or users as much as possible. This will create more individualized and accurate insights.

Create Context – Data silos happen when large, ambiguous groups of data are collected. Ensure that everyone understands what each piece of data actually means to instantly add value to logged data.

Polk County Schools Case Study in Data Analytics

We’ll send it to your inbox immediately!

Polk County Case Study for Data Analytics Inzata Platform in School Districts

Get Your Guide

We’ll send it to your inbox immediately!

Guide to Cleaning Data with Excel & Google Sheets Book Cover by Inzata COO Christopher Rafter