Big Data Ingestion: Parameters, Challenges, and Best Practices

Businesses are going through a major change where business operations are becoming predominantly data-intensive. As per studies, more than 2.5 quintillions of bytes of data are being created each day. This pace suggests that 90% of the data in the world is generated over the past two years alone. A large part of this enormous growth of data is fuelled by digital economies that rely on a multitude of processes, technologies, systems, etc. to perform B2B operations.

Data has grown not only in terms of size but also variety. Large streams of data generated via myriad sources can be of various types. Here are some of them:

Marketing data: This type of data includes data generated from market segmentation, prospect targeting, prospect contact lists, web traffic data, website log data, etc.

Consumer data: Data transmitted by customers including, banking records, banking data, stock market transactions, employee benefits, insurance claims, etc.

Operations data: Data generated from a set of operations such as orders, online transactions, competitor analytics, sales data, point of sales data, pricing data, etc.


Big Data

The gigantic evolution of structured, unstructured, and semi-structured data is referred to as Big data. Processing Big data optimally helps businesses to produce deeper insights and make smarter decisions through careful interpretation. It throws light on customers, their needs and requirements which, in turn, allow organizations to improving their branding and reducing churn. However, due to the presence of 4 components, deriving actionable insights from Big data can be daunting. Here are the four parameters of Big data:

  • Volume: Volume is the size of data, measured in GB, TB and Exabytes. Big data is increasing in terms of volume and heaps of data is generating at astronomical rates. Conventional methods fail to tackle such large volume data.
  • Velocity: Velocity indicates the frequency of incoming data that requires processing. Fast-moving data hobbles the processing speed of enterprise systems, resulting in downtimes and breakdowns.
  • Variety: Variety signifies the different types of data such as semi-structured, unstructured or heterogeneous data that can be too disparate for enterprise B2B networks. Videos, pictures etc. fall under this category.
  • Veracity: Veracity refers to the data accuracy, how trustworthy data is. Analyzing loads of data that are not accurate and contain anomalies is of no use as it corrupts business operations.

The 4Vs of Big data inhibits the speed and quality of processing. This leads to application failures and breakdown of enterprise data flows that further result in incomprehensible information losses and painful delays in mission-critical business operations. Moreover, an enormous amount of time, money, and effort goes into waste while discovering, extracting, preparing, and managing rogue data sets. Additionally, business is not able to recognize new market realities and capitalize on market opportunities.

Big data: Architecture and Patterns

The Big data problem can be comprehended properly using a layered architecture. Big data architecture consists of different layers and each layer performs a specific function. The architecture of Big data has 6 layers.

  1. Data Ingestion Layer: In this layer, data is prioritized as well as categorized. This layer ensures that data flows smoothly in the following layers.
  2. Data Collector Layer: This layer transports data from data ingestion layer to rest of the data pipeline.
  3. Data processing Layer: Data is processed in this layer to route the information to the destination.
  4. Data Storage Layer: In this layer, the processed data is stored.
  5. Data query Layer: In this layer, active analytic processing occurs. In actuality, this layer helps to gather the value from data.
  6. Data Visualization Layer: In this layer, users find the true value of data.

Big Data Ingestion

Data ingestion, the first layer or step for creating a data pipeline, is also one of the most difficult tasks in the system of Big data. In this layer, data gathered from a large number of sources and formats are moved from the point of origination into a system where the data can be used for further analyzation.

Need for Big Data Ingestion

Ingestion of Big data involves the extraction and detection of data from disparate sources. Data ingestion moves data, structured and unstructured, from the point of origination into a system where it is stored and analyzed for further operations. It is the rim of the data pipeline where the data is obtained or imported for immediate use.

Data can be either ingested in real-time or in batches. Real-time data ingestion occurs immediately, however, data is ingested in batches at a periodic interval of time.

Effective data ingestion process starts with prioritizing data sources, validating information, and routing data to the correct destination.

Data Ingestion Parameters

Data ingestion has 4 parameters.

  • Data velocity: It concerns the speed at which data flows from various sources such as machines, networks, human interaction, media sites, social media. This movement can either be massive or continuous.
  • Data frequency: Data frequency defines the rate in which data is being processed. Data can be processed in real-time or batch. In the real-time, data is moved immediately. Whereas, in batch processing, data is stored in batches first and then moved.
  • Data Size: It implies the volume of data which is generated from various sources.
  • Data Format: Data can have many formats, structured, semi-structured, & unstructured.

Challenges of Data Ingestion

With the rapid increase in the number of IoT devices, volume and variance of data sources have magnified. Hence, extracting data especially using traditional data ingestion approaches becomes a challenge. It can be time-consuming and expensive too. Other challenges posed by data ingestion are –

  • Data ingestion can compromise compliance and data security regulations, making it extremely complex and costly. In addition, verification of data access and usage can be problematic and time-consuming.
  • Detecting and capturing data is a mammoth task owing to the semi-structured or unstructured nature of data and low latency.
  • Improper data ingestion can give rise to unreliable connectivity that disturbs communication outages and result in data loss.
  • Enterprises ingest large streams of data by investing in large servers and storage systems or increasing capacity in hardware along with bandwidth that increases the overhead costs.

Data Ingestion Practices


In the days when the data was comparatively compact, data ingestion could be performed manually. A human being defined a global schema, and then a programmer was assigned to each local data source. Programmers designed mapping as well as cleansing routines and ran them accordingly. However, with data increasing both in size and complexity, manual techniques can no longer curate such enormous data. In fact, data ingestion process needs to be automated. Automation can make data ingestion process much faster and simpler. For example, defining information such as schema or rules about the minimum and maximum valid values in a spreadsheet which is analyzed by a tool play a significant role in minimizing the unnecessary burden laid on data ingestion. Many integration platforms have this feature that allows them to process, ingest, and transform multi-GB files and deliver this data in designated common formats. With an easy-to-manage setup, clients can ingest files in an efficient and organized manner. As opposed to the manual approach, automated data ingestion with integration ensures architectural coherence, centralized management, security, automated error handling and, top-down control interface that helps in reducing the data processing time. Integration automates data ingestion to:

  • process large files easily without manually coding or relying on specialized IT staff.
  • alleviate manual effort and cost overheads that ultimately accelerate delivery time.
  • get rid of expensive hardware, IT databases, and servers.
  • handle large data volumes and velocity by easily processing up to 100GB or larger files
  • deal with data variety by supporting structured data in various formats, ranging from Text/CSV flat files to complex, hierarchical XML and fixed-length formats
  • tackle data veracity by streamlining processes such as data validation, cleansing along with maintaining data integrity.

Artificial Intelligence

Apart from automation, manual intervention in data ingestion can be eliminated by employing machine learning and statistical algorithms. In other words, artificial intelligence can be used to automatically infer information about data being ingested without the need for relying on manual labor. Eliminating the need of humans entirely greatly reduces the frequency of errors, which in some cases is reduced to zero. Data ingestion becomes faster and much accurate.


In a host of mid-level enterprises, a number of fresh data sources are ingested every week. In such cases, an organization that functions on a centralized level can have difficulty in implementing every request. Hence, there is a need to make data integration self-service. In doing so, users are provided with ease of use data discovery tools that can help them ingest new data sources easily. In addition, the self-service approach helps organizations detect and cleanse outlier as well as missing values, and duplicate records prior to ingesting the data into the global database.


In the last few years, Big data has witnessed an erratic explosion in terms of volume, velocity, variety, and veracity. Such magnified data calls for a streamlined data ingestion process that can deliver actionable insights from data in a simple and efficient manner. Techniques like automation, self-service approach, and artificial intelligence can improve the data ingestion process by making it simple, efficient, and error-free.

Your Definitive Guide To Building Valuable Narratives Through Data Storytelling

Stories inspire, engage, and have the unique ability to transform statistical information into a compelling narrative that can significantly enhance business success.

By gaining centralized access to business data and presenting it in a visual way that follows a logical path and provides invaluable insights on a particular area or subject, you stand to set yourself apart from your competitors and become a leader in your field.

Here, we’ll explore the unrivaled power of data storytelling in the digital age while looking at a mix of powerful data storytelling examples generated with a modern dashboard creator.


Exclusive Bonus Content: Your definitive guide to data storytelling! Download our free executive summary and start creating your stories!

What Is Data Storytelling?

Storytelling through data is the process of transforming data-driven analyses into a widely-accessible visual format to influence a business decision, strategy, or action by utilizing analytical information that, ultimately, turn into actionable insights.

Beyond this data storytelling definition, the power of a data story lies in our natural affinity for plotlines and narratives that convey information. By leveraging the right tools, it’s possible to take quantitative metrics or information, arrange it into a logical format, and create a narrative that simplifies complex information, presenting it in a way that engages a particular target audience.

Data storytelling has a host of business-boosting benefits.

The Benefits Of Data Storytelling

Tales help make sense of the world around us, and this very notion is the beating heart of using data to tell a story.

According to a study performed by Skyword, content that features a mix of words and visuals drives 34% more engagement than text-only articles, blog posts, or whitepapers. You have everything to gain by harnessing the power of data visualization, visual analytics and using a mix of relevant insights to create a compelling narrative.

Here are the key benefits of knowing how to tell stories with data:

  1. Inclusion: As mentioned, at a fundamental level, stories help us make sense of a complex and occasionally bewildering world. By using the right data storytelling tools to measure, track, and extract relevant data and place it into a visual format that fits into a narrative based on specific business goals, you will make your analytical information accessible to a wider audience. By doing so, you’ll be able to share important messages in a way that inspires, encouraging buy-in from the right parties or stakeholders as a result.
  2. Decision: By telling a data story through a powerful KPI dashboard software, you’ll be able to drive improved decision-making throughout the organization in several critical areas of the business. If your audience, whether internal or external to the organization, can follow a narrative and extract the right information from your presentation, they’ll gain the insight they need to base their strategies on water-tight data, making the organization more efficient, economical, and successful as a whole.
  3. Organization: In a world dominated by data, knowing which insights to explore can prove daunting. But by working with the right data storytelling tools, not only is it possible to simplify the analytics process, but you’ll also gain the ability to arrange your data in a way that’s effective, efficient, and ultimately saves you time. As you’re no doubt aware—in business, time is money.
  4. Action: If you tell stories with data and tailor your presentations to your target audience, you’ll drive actionable results. If the person is inspired by what you have to show them, and they understand it on a deep, meaningful level, they will act in the desired way. For instance, if your audience is internal, they may formulate an initiative that helps enhance the company’s marketing efforts. Or, if you’re presenting to external stakeholders through storytelling with data, you might prompt them to increase their investment.

“Storytelling is the essential human activity. The harder the situation, the more essential it is.” - Tim O’Brien, author

Exclusive Bonus Content: Your definitive guide to data storytelling! Download our free executive summary and start creating your stories!

How To Tell Stories With Your Data?

It’s clear that storytelling with data is powerful. To place the notion of knowing how to tell stories with data into practical perspective, here we look at a mix of data storytelling examples or concepts, backed with actionable advice as well as genuine data storytelling templates.

a) Turn metrics into actionable concepts

As we’ve explored, knowing how to tell a story with data will empower you to turn metrics into actionable concepts or insights.

One of the most effective ways of transforming quantitative data into a results-driven narrative is by working with key performance indicators (KPIs).

By harnessing the power of an interactive business intelligence (BI) dashboard, you’ll be able to select the KPIs that align with your core business goals, using the perfect mix of graphs, charts, and visuals to build a narrative that brings your data to life.

To get under the skin of this most priceless concept, read our guide to data-driven dashboard presentation.

b) Improve processes with plotting

Every solid story, regardless of its theme or format, has a definitive plot: a beginning, a middle, and an end.

By using data storytelling templates, tools, and platforms, you can populate your plot with the visualizations that will drive the narrative forward while conveying your message in the most effective way possible.

To improve your processes with plotting, you should sit down in a collaborative environment and consider the primary aim of your data-driven story while outlining the beginning, middle, and end.

With your framework firmly in place, you should start to populate your plot with the KPIs and visualizations that not only represent what you have to say but are also most relevant to the data you’re looking to present.

By working through your plot logically and fleshing it out with the right visualizations from your dashboard, you’ll help streamline processes within your organization, increasing efficiency and productivity as a result.

c) Simplify & make connections

If your business is informed, well-oiled, and strategic across the board, you will grow, evolve, and boost your profits over time.

By harnessing the power of storytelling through data, you’ll be able to connect the dots, simplifying ideas and making the kind of connections that will give your business a newfound sense of strategic direction.

To squeeze the maximum benefit from your data storytelling efforts, you should focus on creating an interactive dialogue between your insights and your audience, using a mix of historical, real-time, and predictive data to drive your message home, whether for financial reporting processes or strategic development of the company.

Moreover, you should create a balance or harmony between your words and your visuals to make it easier for your audience to make the necessary connections that will result in business-enhancing actions.

The most powerful way of creative data-driven narratives that simplify insights is to take a “storytelling with data visualization” approach to your efforts. Now, we’re going to explore this invaluable concept in action.

Tell Stories Through Data Visualization

Storytelling with data through data visualization is the best way to share stories with your audience. It’s the glue that binds all of the ideas we’ve mentioned so far.

To demonstrate the power of storytelling with data visualization, here are two strikingly different but equally powerful visuals used for building an effective narrative with your insights.

Employee Performance Dashboard Example

Featured KPIs:

  • Absenteeism Rate
  • Overtime Hours
  • Training Costs
  • Employee Productivity

Primarily used to streamline busy human resources departments, this HR dashboard that focuses on employee performance features a mix of KPIs that build a comprehensive profile around attendance rates, individual productivity, training costs, and overtime hours accrued.

Data storytelling through HR: employee’s performance and behavior.

**click to enlarge**

It’s possible to use this dynamic mix of charts, graphs, and graphical information by utilizing HR analytics tools, and build an effective narrative relating to employee performance over a particular time frame, creating a compelling plot that will lead to increased productivity and enhanced economic efficiency as well as and support strategies that will boost staff engagement exponentially.

By looking at this dashboard and related HR KPIs, it’s easy to see how you could build a plot around this perfect storm of insights. Coupled with the data visualizations featured in related HR-based dashboards, the possibilities are seemingly endless; from creating effective HR reports to obtaining a birds-eye view of the whole human resources processes and development.

Compliance Rate KPI

A valued fulfillment-based KPI across industries, this dynamic mix of graphs offers a panoramic snapshot of supplier compliance rates over a particular time frame.

A key component of our procurement dashboard, the compliance rate KPI is a prime example of how powerful an individual visualization can be in communicating vital information and how it can fit into a broader narrative.

Compliance rate is one of our data storytelling examples focused on the procurement industry, and broken down per type of suppliers.

It’s possible to place this KPI into the heart of a story surrounding procurement structures, success, and processes, offering a breakdown of compliance per supplier in addition to the company’s overall compliance success rate.

Connected with a tailored mix of our additional top 10 procurement KPIs, it’s possible to develop a story that helps to convey key trends, connect organizational dots, and share actionable insights that drive real change. A prime business report example of big data storytelling in action.

How To Create Data Reports That Will Skyrocket Your Business Performance

Historically, the terms data report or business report haven’t got the crowds excited. Data reports have always been important for businesses. However, they have been a necessary evil, created by analysts and consultants. The term usually conjures up images of static PDFs, old-school PowerPoint slides and big tables. Usually created with past data without the possibility to generate real-time or future insights, these reports were obsolete, comprised of numerous external and internal files, without proper data management processes at hand.

It doesn’t have to be this way. The business intelligence industry has been revolutionized over the past decade and data reports are in on the fun. The rise of innovative report tools means you can create data reports people love to read. If you utilize business intelligence correctly, not only you will be able to connect your data dots, but take control of your data across the company and improve your bottom line.


In this post, we will explain what is a data report, how to write one and provide the best possible examples created with modern software. Read on to see why data reports matter and our top data reporting tips.

Your Chance: Want to test a modern data reporting software for free? We offer a 14 day free trial. Benefit from great data reports today!

What Is A Data Report?

Data report is an evaluation tool used to assess past, present, and future business information while keeping track of the overall performance of a company. It combines various business data, and usually used both on an operational or strategic level of decision-making.

As mentioned, these reports had features of static presentation of data, manually written or calculated, but with the introduction of modern processes such as dashboard reporting, they have developed into an invaluable resource to successfully manage your sales processes, marketing data, even robust manufacturing analytics and numerous other business processes needed to stay on top of the pack.

But let’s get into the basics in more detail, and afterward, we will explore data reporting examples that you can use for your own internal processes and more.

Data Reporting Basics

Data analytics is the science of examining raw data with the purpose of drawing conclusions about that information. Data reports present the data, analyses, conclusions, and recommendations in an easy to decipher and digest format. These business reports can cover a wide variety of topics and objectives and can vary greatly in length, content, and format. It can be annual reports, monthly sales reports, accounting reports, reports requested by management exploring a specific issue, reports requested by the government showing a company’s compliance with regulations, progress reports, and feasibility studies.

Historically, creating these business data reports was time and resource-intensive. Data pull requests had to be made to the IT department and a significant amount of time was spent analyzing, formatting and then presenting the data. Because this task was so resource-heavy, it couldn’t be done often. Also, by the time the data was presented, it was generally out of date. The emergence of real-time cloud-based BI reporting tools has changed the data reporting game. Now a wider range of business users can act as analysts, even performing advanced analytics. The right BI platform can blend multiple data sources into one report and analysis: enhancing business insights and better-informed decision making. These cloud-based tools allow organizations to collaborate on a report, bringing various subject matter experts (SME) to the same table. Modern business dashboard tools allow a wider audience to comprehend and disseminate the report findings. Users can also easily export these dashboards and data visualizations into visually stunning reports that can be shared via multiple options such as automating e-mails or providing a secure viewer area, even embedding reports into your own application, for example.

OK, now you are sold on how new data reporting tools are making reports easier to build, more encompassing of disparate data sources, visually powerful and easier to share in various formats. They are also increasing analytic capabilities.

Now, let’s look into some tips and ideas to keep in mind before and when you start to build and create data reports that will enable you to save time, and, ultimately, costs.

How To Write A Data Report?

Depending on the type of the report, each has its own set of rules and best practices. We will mention below the most popular ones, but our main focus is on business data reports that will, ultimately, provide you with a roadmap on how you can make your reports more productive. Let’s get started.

How to create data reports: 1. Define the type of your data report, 2. Know your target audience, 3. Have a detailed plan and select your KPIs, 4. Be objective, when possible, 5. Be visually stunning, 6. Have content sharply written, 7. Make sure the report is actionable, 8. Keep it simple and don’t be misleading, 9. Don’t forget to tell a complete story, 10. Use professional data report software.

1. Define The Type Of Your Data Report

What types of data reporting do you need to present? Having this definition ahead of time will help set parameters you can easily stick to. Here are the most common data report types:

1) Informational vs. analytical: First determine if this report is just providing factual information. Informational reports are usually smaller in size, the writing structure is not strict, and the sole purpose is to inform about facts without adding any analysis. On the other hand, if it is providing any analysis, demonstrates relationships or recommendations, it is an analytical report.

2) Recommendation/justification report: Presents an idea and makes suggestions to management or other important decision-makers. What the name suggests, it provides recommendations to changes in business procedures and justifies courses of actions that have the goal of improving business performance.

3) Investigative report: Helps determine the risks involved with a specific course of action. Here, reporting data is based on documenting specific information objectively with the purpose of presenting enough information to stakeholders. They will ultimately decide if further actions are needed. An example would be a report created for legal purposes.

4) Compliance report: Shows accountability by providing compliance information for example to a governing body.

5) Feasibility report: An exploratory report to determine whether an idea will work.

6) Research studies report: Presents in-depth research and insights on a specific issue or problem.

7) Periodic report: Improves policies, products or processes via consistent monitoring at fixed intervals, such as weekly, monthly, quarterly, etc.

8) KPI report: Monitors and measures Key Performance Indicators (KPIs) to assess if your operations deliver the expected results.

9) Yardstick report: Weighs several potential solutions for a given situation.

2. Know Your Target Audience

Knowing your audience will help determine what data you present, the recommendations you make and how you present the data. Your audience may be upper, middle or line management, other departments in the company, coworkers, the client, potential clients, the government or another company in the same market. Knowing your audience helps determine what type of information to include in the report. If a report is internal facing, branding such as colors, font, and logo aren’t as crucial. If it is a one-time live presentation, formatting for printing isn’t key. Determine ahead of time if your audience needs persuasion or education. If your audience is C-suite level or the board, you may want to present mostly high-level data with specific call outs and action items. If the report is more exploratory in nature, you may want to include more granular data and options to interact with the data. Ramon Ray, tech evangelist and founder of Smart Hustle Magazine, wrote about how to best present your data to a wide audience. He focused on keeping text simple, use visualizations whenever possible, including video and animation when appropriate, and making your reports/presentations interactive. Knowing your audience before you start your analysis – and even more importantly before you put together the report – will keep your reports and data focused and impactful.

3. Have A Detailed Plan And Select Your KPIs

We are going to sound like a broken record here, but have a report plan before you start your analysis. What information does the management need for its effective decision making? What data and insights do your shareholders require? Understand the scope of data required and think about how you will want to use that data. Utilize as many data sources as possible. But don’t go data crazy and get bogged down in unnecessary information. Of course, you have to remain agile and may have to adapt the plan, but a robust plan is crucial. Remaining purpose-driven will focus your work, save you time in the long run and improve your business reporting outcomes.

When creating your plan, it is crucial to select the right KPIs. You don’t need dozens of metrics that will answer all your business questions at once, but pick a few that will tell a comprehensive data story (more on that later), and enable you to take proper action (more on that later, too). Depending on your department or industry, reports will vary as KPIs also vary, but choose the ones that will help you put your data into proper context and always keep in mind the audience you’re addressing.

Your Chance: Want to test a modern data reporting software for free? We offer a 14 day free trial. Benefit from great data reports today!

4. Be Objective, When Possible

A good business data report describes the past, present or possible future situation in an objective and neutral way. Objective means the report states facts, not an opinion. Keep the opinions minimal. It helps to combine them in one section, possibly titled “Suggested Actions.” Also, using a passive voice in a report will help keep the report formal and objective. For example:

Active: The managers need to make changes in their management style. Passive: Changes in management style need to be made.

5. Be Visually Stunning

Numerous types of data visualization have proven to be extremely powerful. Analytics presented visually make it easier for decision-makers to grasp difficult concepts or identify new patterns. Data presented visually are easier for humans to perceive and digest. Reports should include data visualizations over text whenever possible. Just make sure you are choosing the most appropriate data visualization to tell your data story and that you are following BI dashboard best practices. With the right data reporting tool, anyone can create meaningful visuals and share them with their team, customers and other shareholders. All this can be accomplished without involving a data scientist.

Also, make sure your report remains visually stunning, no matter how it is shared and disseminated. Your report should look good on a computer, tablet, PDF or even on a mobile screen. That’s why utilizing a dashboard can be the most cost-effective solution that will provide you with not only stunning visuals but interactivity as well.

Bonus tip: For reports like annual reports that will be printed and widely shared, the extra focus should be spent on dashboard design principles.

6. Have Content Sharply Written

While the focus should be on visuals, some data report types also need text. Make sure your reports use persuasive and even-toned business writing. Use concise, active and engaging language. Use bullet points versus long paragraphs. Use headers and provide legends and supplementary text for your visualizations. Also, you should always proofread!

7. Make Sure The Report Is Actionable

Prescriptive, descriptive, and predictive analytics are becoming increasingly popular in recent years. Each brings new insights needed to make better business decisions and increase ROI – insights from the past, future, and prescribing possible outcomes. That being said, make sure your report has a conclusion. When necessary, provide recommendations. Reports should be objective but the best ones are also actionable. Intended audiences should walk away with the next steps or greater insights. By doing so, you will enable a data-driven business environment and foster a more efficient collaboration.

8. Keep It Simple And Don’t Be Misleading

While data should be objective, formatting, filtering, and manipulation can be easily misleading. Make sure you are being consistent and reliable with your reporting. Also, keep it simple. The boom of data visualization and reporting tools has led to the creation of visualizations that don’t tell a data story. You shouldn’t need 3-D glasses to read a report. Sometimes, a simple chart is all you need. You also don’t need to go nuts with colors and formats. You can easily overwhelm your audience this way. Choose a couple of colors that are easy on the eyes. Keep to one font. Don’t go crazy with highlighted, bold or italicized text. You don’t have to create a “piece of art” for your report to be visually stunning and impactful.

Your Chance: Want to test a modern data reporting software for free? We offer a 14 day free trial. Benefit from great data reports today!

9. Don’t Forget To Tell A Complete Story

To successfully report data, you must take into account the logic of your story. The report should be able to provide a clear narrative that will not confuse the recipient but enable him/her to derive the most important findings.

Consider creating a dashboard presentation. That way you will have your data on a single screen with the possibility to interact with numerous charts and graphs while your story will stay focused and effective. By utilizing interactive visualizations, you not only have a strong backbone on how to write a data report but also ensure that your audience is well-informed and digests data easily and quickly.

10. Use Professional Data Report Software

Last but not least, utilizing a modern visual analytics software will ensure you design your reports based on the decisions you need to make, filtering the ever-present noise in reporting processes and making sure you don’t get lost in the details. Often times, reports are piled with large volumes of spreadsheets and presentation slides that can create an obscure view on the presented data, and increase the possibility of (unintentional) errors. The software can eliminate hideous manual tasks of searching through rows and columns, provide the necessary real-time view, alongside with the possibility to look into the past and the future of how the data will behave.

No matter if you’re an analyst working with databases and need a strong MySQL reporting tool or a marketing professional looking to consolidate all your channels under the same data-umbrella, the software will enable you to clear the clutter and automate your reports based on your specific time intervals. They will update the data automatically, and you will not need more than small refinements to make sure the data you present is the one your audience needs.

We have expounded on the data reports definition, saw the top 10 best practices to create your own, and now we will continue our focus on data reports examples from a few industries that will present these practices in action.

Data Reports Examples And Templates

To be able to create reports that drive action and provide added value to your company’s business efforts, here are some examples that put the reporting creation and presentation in perspective. These examples are created with the help of a professional dashboard designer that empowers everyone in the line of business to build their own reports. Let’s start with the finance department.

  1. Financial KPI dashboard

Finance is the beating heart of any business and creating a financial report is the basis for sustainable development. Companies need to keep a close eye on how their monetary operations perform and make sure their financial data is 100% accurate.

Our example focuses on KPIs that are meticulously chosen to depict the general financial health of a company. The displayed information is presented in a logical order, connecting various financial KPIs that make a complete data story, without the need to overcrowd the screen or complicate the report.

Data report example from the financial department.

**Click to enlarge**

What is data reporting doing in this case is quite simple. Presenting the most important information in a clear financial narrative that will drive action. We can see in this financial dashboard that the company managed to decrease the cash cycle, but the vendor payment rate had a spike in September last year. It might make sense to take action and see in more detail what happened so that the processes can be adjusted accordingly.

  1. Retail KPI dashboard

Retailers must be extra careful in picking the right KPIs and presenting their data into a clear order, without cluttering the report or confusing the people that need to read it and act accordingly.

Data reporting examples from the retail industry.

**Click to enlarge**

A retail dashboard such as the one presented above focuses on the perspective of orders which is one of the crucial points in this cutthroat business.

Gaining access to these business touchpoints will equip you with the best possible ingredients to stay competitive on the market. By utilizing KPIs such as the rate of return (also by category), customer retention rate and the number of new and returning customers, will enable you to access in-depth information on your order processes and ensure your actions stay focused on developing your business on a sustainable level. For example, you can keep an eye on the rate of return and make sure it stays as low as possible. That way, your costs will be significantly lower and, ultimately, customers more satisfied.

Your retail analytics processes don’t need to foster complex reports, but an example such as we presented above, you can see that reporting with dynamic visualizations empowers you to make better business decisions.

Your Chance: Want to test a modern data reporting software for free? We offer a 14 day free trial. Benefit from great data reports today!

Start Building Your Data Reports Now!

Reporting, analytics, and information delivery can have a transformational impact on an organization if implemented correctly. Luckily, the mind-numbing task of manually creating daily or weekly reports is a thing of the past. With the right plan and proper business reporting software, you can easily analyze your data and also create eye-catching and remarkable reports.

13 Analytics & Business Intelligence Examples Illustrating The Value of BI

Digital data, by its very nature, paints a clear, concise, and panoramic picture of a number of vital areas of business performance, offering a window of insight that often leads to creating an enhanced business intelligence strategy and, ultimately, an ongoing commercial success.

Business intelligence steps up into this process by creating a comprehensive perspective of data, enabling teams to generate actionable insights on their own. With the introduction of online BI, companies today have the chance to create additional value, and, ultimately, profit.

At its core, business intelligence (BI) encompasses the strategies and technologies used by companies for the detailed online data analysis of key business-based information. BI technologies offer historical, current, and predictive insights into various aspects of business operations, thus helping a company to make informed decisions on activities centered around finances, marketing, sales, competitor research, social outreach, internal processes and more.


Exclusive Bonus Content: Business Intelligence Examples: A Summary For true business intelligence inspiration, download these BI examples!

Business intelligence is vital in our digitally-driven world as it essentially gives you an additional sense: a commercial vision that can help you see and process far more than the information that presents itself on the surface. And there are business intelligence examples and insights out there that demonstrate that every notion.

To put the power of business intelligence into perspective, here are 4 key insights you should know:

  • Businesses using analytics are five times more likely to make better, quicker decisions, according to an article published on BetterBuys.
  • By 2025, the global BI and analytics market is expected to soar to a worth of $147.19 billion, growing at a CAGR of 26.98% from 2016.
  • Businesses will create and manage 60% of the world’s data by 2025.
  • 85% of business leaders believe that big data will change the way they do business, significantly, especially in the personalization potential of intelligence.

It’s clear that BI and the tools that facilitate better business intelligence are vital to the future of any company competing in the digital arena, regardless of industry or sector.

Here we explore 13 BI examples based on real-life case studies, scenarios, data, and discoveries. These business intelligence examples will showcase the power and potential of big data analytics in the modern age and how it can make your venture smarter, stronger, more scalable and more successful.

Without further ado, here are 13 inspirational examples of business intelligence.

1) Improving The Decision-Making Process

One of the primary benefits of BI is the ability to make better and more valuable decisions, and this business intelligence example is based on that very idea.

In the first of our business analytics examples, the CEO and founder of a budding fintech company was presented with the challenge of changing his business’s internal culture with a view to making all business data more accessible across the board.

To avoid the IT department having sole control over the data, and thereby preventing other departments from working collaboratively and making informed decisions that benefit the business, the company’s CEO deployed a dashboard reporting software for an automated data reporting process. As a direct result of this decision, not only is the company’s data now decentralized and digestible, improving the decision-making process across the board, but it has also saved 40 valuable hours per week on report preparation. This is one of our business insights examples that don’t stop here.

Speaking on this BI triumph, the fintech CEO said, “All departments now can access their own real-time dashboards, no matter if they are in the office or at a meeting. All decision-makers have quick, easy access to ad-hoc analysis and reports, even on their tablets.”

2) Uncovering Fresh Business Insights

The second of our business analytics examples is focused on discovering new business insights that can ultimately help streamline commercial processes, thereby improving productivity and boosting the bottom line.

One of our business intelligence examples explains the usage of analytics in the food industry

A forward-thinking online food ordering business wanted to gain a better insight into the life cycles of its customers while gaining the ability to optimize sales reports and marketing campaigns in a time-efficient, cost-saving, and autonomous way.

By gaining self-service access to real-time analytical information the company was able to streamline its marketing and sales activities, make better, swifter decisions based on real-time information and uncover fresh insights that have served to improve its level of customer experience, resulting in increased brand loyalty.

The use of a real-time dashboard has empowered the budding online food giant to monitor all significant business operations through customized KPIs. Moreover, the new business analytics platform has made the business more able to rise to challenges as they unfold in days, rather than weeks or even months later. With the help of sales graphs and charts, the data was easily interacted with, and presented on a single screen.

This is one of our examples of business analytics that demonstrates how quickly the power of business intelligence affects the decision-making processes and creates a backbone for sustainable growth.

3) Boosting Productivity

Today’s consumers crave ratings, opinions, and reviews from their peers to help them make decisions, particularly when it comes to travel. That said, a travel-based rating business should be able to deliver an exemplary level of customer experience and support to its users.

In the third of our business intelligence examples, a hotel rating company based in Berlin turned to business intelligence analytics software as its data was fragmented, diluting its impact across the company, impairing its productivity and service levels as a result.

By rolling out a SaaS-based analytics solution that requires minimal IT intervention and making it accessible across the organization, the travel company was able to consolidate its key data in one accessible space.

When it comes to big data examples in real life, this travel business made a wise BI-based move that resulted in improved internal efficiency, better interdepartmental cohesion, and the new level of insight has also enhanced the company’s level of customer support beyond the CEO’s wildest expectations.

4) Increasing Sales

Number 4 of our inspiring BI examples demonstrate that by using big data analytics to your advantage, you can increase your sales – which is one of the primary aims for all business worldwide. By taking advantage of well-established sales KPIs, each business can improve its bottom line. Let’s see this through one of our top examples of companies using business intelligence.

A rising online retail player was suffering from an inconsistent and somewhat erratic sales performance for some time and was unable to evolve its strategy despite a host of efforts.

After turning to BI methodologies and incorporating a sales dashboard to solve this ongoing issue, it became apparent, almost immediately, that force rather than data drove the company’s sales strategy. By realigning its strategy and drilling down into the data available at its fingertips, the company’s sales grew by 24% while rep attrition fell by 90%. A better organized target-setting process, as well as streamlined sales strategies driven by data, has ensured that the company not only continues to scale, but its sales team continues to surpass its targets.

5) Improving Financial Efficiency

Without a doubt, your financial department is one of the beating hearts of your organization because, without steady cash flow and the capital to invest back into the business, the entire operation would grind to halt.

Business analytics examples in finance showcase how reports and BI methodologies can effectively benefit a business

That said, the fifth of our business analytics examples focuses on evolving a company’s business intelligence to identify potential cash flow issues and improve financial efficiency. In this scenario, a disease diagnostics brand turned to an online reporting software and business intelligence methodologies as, despite a period of rapid growth, the company’s percentage collections were low, and accounts receivables, as well as claim denials, had reached a record high.

To tackle this potentially devastating issue, the company implemented an intuitive financial reporting system that allowed them to drill down into a wealth of relevant account-based metrics but also utilizing a wealth of financial graphs that helped them see data in a visual and straightforward way.

As a result of this savvy BI initiative, and the most financially-driven of our examples of business intelligence, the business diagnostics was able to, well, diagnose the issues by leveraging the power of financial reports, uncovering the source of the claim denials, and recovering millions of dollars’ worth of claims in the process.

6) Streaming Internal Processes

A rapidly growing US-based healthcare company suffering from a raft of disjointed internal processes and commercial inefficiencies sought the power of BI in the sixth of our analytics examples.

Due to a lack of cross-departmental compatibility arisen out of poor data handling, collection, and analytics processes, the business was unable to use this wealth of digital insight to its advantage. However, by working with a BI partner to develop and deploy a unified business intelligence system that integrates multiple data sources into one single platform, the healthcare company was able to analyze their data in on an efficient, valuable and accessible format, empowering it to make increasingly smart decision to benefit the business. This is one of our business insights examples that shows us how healthcare companies can perform on a much higher level.

Armed with the tools required to perform their jobs better, departments including finance, billing, marketing, and sales began to work more productively, evolving internal processes, and boosting cross-departmental collaboration.

This scenario is perhaps one of the most valuable of our business analytics examples as it serves to showcase how big data in healthcare and business intelligence can help foster a culture of continual evolution, which is an invaluable asset in today’s fast-paced digital world.

Exclusive Bonus Content: Business Intelligence Examples: A Summary For true business intelligence inspiration, download these BI examples!

7) Saving Marketing Dollars

One of our industry BI solutions examples focuses solely on the marketing department of a company. A US-based e-commerce company suffered from low conversion rates, despite the fact their campaigns were monitored on a daily, weekly, and monthly basis with the help of traditional spreadsheets. The team was disparate across 19 US cities, and their communication was oftentimes slow. By the time the campaign manager analyzed the campaign data, another campaign already needed to be launched. This blind approach to their promotional activities cost them days and weeks of proper planning, analyses, and reporting process. Losing time in marketing is a significant issue that costs dollars, and, ultimately, conversions.

A centralized BI solution saved the team 4 working days per week by automating the reporting processes, alarming the designated campaign manager when an anomaly in the campaign occurred, and predicting future campaign results.

They utilized a similar marketing dashboard such as this one:

One of business intelligence examples showing the marketing performance of specific campaigns.

**click to enlarge**

This example of business intelligence shows the top 3 campaigns by spent budget, the total number of impressions, clicks, acquisitions, and the CPA by a campaign for the last 12 weeks. The whole team had this dashboard automatically delivered and updated in their inbox each week – insights were made fast, campaigns were planned better, and campaign managers across the US had the same data at the same time so their communication and cohesion also improved.

8) Reducing IT Involvement

A financial company was having difficulties in their analytical processes involving the IT department. Their daily analytical and ad hoc reports were often times late, and employees didn’t have a proper insight into the massive amounts of data they were collecting. The IT department was simply overburdened with requests from each department of the company, and when you add the additional tasks they needed to handle, they were overpowered with a shortage of time and efficiency.

The company wanted to decentralize the decision-making process, and grant business users across the company the possibility to extract, administer, and derive insights while creating their own reports, without the need to wait for the IT department for hours, even days. Since they needed to combine multiple data sources, internal and external, a business intelligence solution was a logical step forward for implementing into their operational and strategic management.

The company quickly saw an improvement of their reporting and analytical processes, not only via automated standard reports but also in ad hoc analysis, where they only needed to utilize a drag-and-drop interface to generate immediately actionable insights. The IT department could also resort to the database reporting tool since business intelligence provides beginner and advanced features for business users across industries and departments.

This is one of the business analytics examples that show how to unburden staff and create a working culture that saves time and increases productivity.

9) Connecting Departments

Another real-world business analytics example centers around a fashion label based in Washington, D.C. with multiple stores across the city.

Their challenges arose when they needed to combine sales and marketing data in real-time, optimize their promotional activities to deliver the best possible results, and create a comprehensive overview of the customer lifecycle. That meant that a vast amount of data and KPIs needed to be managed and successfully analyzed to get the best possible results.

They decided to implement business intelligence into their operations to be able to monitor real-time data, ensure employees have access to marketing and sales analytics and use a dashboard builder to visualize all their business information.

It not only improved customer loyalty (we will talk about this in another example of business intelligence), but by connecting various datasets from different departments, the company managed to utilize these business intelligence KPI tools for various purposes such as aligning promotional activities with the goal of closing more sales deals. Ultimately, the sales department was better informed about marketing activities, and the marketing department could better plan their promotions to fit the overall customer lifecycle.

Exclusive Bonus Content: Business Intelligence Examples: A Summary For true business intelligence inspiration, download these BI examples!

10) Increasing Employee Performance

We continue with examples of companies using business intelligence by mentioning an HR department of a US-based company that was experiencing issues when employees started to increase their overtime hours, the productivity decreased, but the number of sick days steadily grew. This was an unusual situation in HR since it showed them that there are issues with the workforce, but they couldn’t afford the time to speak with each employee, it would have taken them weeks of time, even months.

The manager decided to take advantage of HR analytics software and utilize business intelligence for their department. By analyzing workforce behavior and performance, the department had a better overview of the issues that needed to be solved.

The most prominent HR KPIs that were looked upon are the overall labor effectiveness, overtime hours, absenteeism rate, and the training costs to see if new hires would make sense. After looking at the data on interactive visualizations that gathered historical and present information of the whole department, the manager could clearly conclude that the department was understaffed, chronically tired, and the company needs new hires or the sick days will increase exponentially, thus creating substantial business issues in the long run.

This is one of our operational business intelligence examples that showed us how workforce management can be streamlined and upscaled so that the whole company doesn’t deteriorate because the staff simply needs more help.

They used an HR dashboard similar to this example below:

One of business analytics example that focuses on the employee’s performance and behavior.

**click to enlarge**

This enabled them to advance their employee productivity and the overall performance since the analytics process empowered the manager to make a better-informed decision and create regular HR reports that provide accurate data. And it didn’t take weeks or months to do it, visualizations were generated with a few clicks.

Now we will focus on examples of business analytics that improved a manufacturing business located in France.

11) Enhancing Manufacturing Processes

A plethora of data is managed in the manufacturing industry, and when you add the increased use of robotics and artificial intelligence, this industry is one of the pioneers when it comes to utilizing business intelligence.

The issues this particular German-based company was facing were related to streamlining their production process since they started to experience more problems with their equipment so the production volume decreased, and business concerns started to arise.

By having a birds-eye view of all the manufacturing analytics needed to successfully operate the production process, the company managed to make the most out of intelligent data alerts that enabled the production workers to be immediately alarmed when an anomaly would occur. This ensured that no machinery is out of order any more as their repairs and management could go under immediate inspection, even before the actual breakdown occurred, the production process stopped, and enormous amounts of revenue lost.

This is one of our real-world business analytics examples that puts a spotlight on artificial intelligence, and how it improves the maintenance of production facilities that need the lowest production downtime possible, one of the most important manufacturing KPIs, alongside with the production volume and costs.

Now we will take a look at our next business intelligence solutions examples focused on retail and a web-based electronics supplier.

12) Improving Customer Loyalty

In number 12 of our business analytics examples, a clothing retailer in the early stages of its rising success was struggling to scale its business further. After a year of impressive growth, the business saw its profits and customer acquisition levels plateau while seeing a rise in customer churn. The company was able to maintain its momentum so it decided external help was needed for continued success.

Clothing retail is one of bi examples that shows how to overcome early stage business struggles

By opting for a retail dashboard customized to display a host of invaluable demographic data about its existing users and target audience and with retail KPIs focused on increasing customer value and new customer acquisition, the company began to grow its audience once again.

The business was able to identify its strengths and weaknesses, spot emerging trends, and segment its audience accurately to ensure it offered the right personalized deals or offers to the right set of consumers, resulting in significant growth in its customer base and increased brand loyalty.

13) Optimizing Inventory

The thirteenth and final of our operational business intelligence examples, or business analytics examples, is centered on stock or inventory optimization. Around 46% of SMBs either don’t track their inventory or use a laborious manual method to do so, costing time, money, and a host of other valuable resources.

A tight-knit web-based electronics supplier that deals with a large warehouse of ever-changing stock began to feel the effects of a poor inventory management process when it began to lose track of high-value items and ensure an increasing number of in-house damages.

Before the situation spiraled out of control, the company adopted a decision support system (DSS) so it could use all of its inventory-based data to make informed choices regarding the way it stored, quantified, and managed its stock on a sustainable basis. This exhaustive and incredibly smart analysis of historical data in addition to stock-taking metrics for warehouse product not only helped the business to keep track of its various items, but it also prevented damages and ensured all of its popular products remained stocked at all times.

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