Are we experiencing the Modern Business Intelligence Renaissance?
The last decade has seen the Business Intelligence (BI) space transform from a niche specialty into a cornerstone of modern business operations. With the data revolution, companies recognized the need for detailed insights to steer strategies. This need gave rise to a myriad of BI tools, each promising to unlock the secrets hidden within vast data lakes.
Whether you’re a data scientist, marketing professional, or bored (board) level tinkerer, there seems to be a BI tool to meet your needs.
The Titans of BI: More Than Just Tools
To truly appreciate the need for a unified approach, we probably need to understand why a company would choose to have more than one BI tool. Surely they all do the same thing, right? Spoiler: They don’t and don’t call me Shirley.
Here’s a quick breakdown of the most popular BI tools and their uses:
Tableau: Beyond its striking visualizations, Tableau champions a user-friendly experience. With drag-and-drop functionality, even those less acquainted with analytics can uncover patterns, offering a democratized approach to data-driven insights.
Power BI: While integration with the Microsoft suite is a key strength, Power BI also brings advanced data modeling capabilities. The tool’s DAX (Data Analysis Expressions) language allows for sophisticated calculations, making it a powerful tool for those with a deeper understanding of analytics.
Looker: Not just a visualization tool, Looker excels in its data modeling layer. It offers a unique perspective by turning database queries into reusable code chunks, enhancing efficiency and consistency across the board. It also benefits by having the backing of a tech giant like Google.
Qlik: Beyond its associative data modeling, Qlik’s in-memory data processing delivers rapid-fire analytics results, catering to businesses needing real-time insights.
The Fragmentation Problem: A Blessing and a Curse
While choice is beneficial, too much of it can lead to that old classic, operational inefficiencies. If your organisation features multiple departments using different tools then I’m afraid to say that you may have come down with one of the following affiliations:
Siliosis (Operational Silos): Where one department’s insights remain inaccessible or incoherent to another due to the BI tool they’re using.
Challenge-itis (Training Challenges): Onboarding new employees becomes challenging when they must familiarize themselves with multiple BI tools.
The Wallet Flu (Financial Overheads): Managing licences and updates for multiple tools can become a logistical and financial burden.
Unified Analytics: The Meta-Layer Revolution
Unified analytics platforms, like Digital Hive, are not about replacing these BI tools but embracing them. Digital Hive serves as an overlay, ensuring the diverse BI tools communicate effectively. Think of it as a mediator that features some great real world applications and advantages, like:
Streamlined Reporting: Imagine a global enterprise where the European arm uses Looker due to legacy reasons, while the North American arm swears by Tableau. Unified analytics allow for a consolidated dashboard that executives can use to gauge global performance metrics without delving into the specifics of each tool.
Leveraging Strengths: A unified platform recognizes that every BI tool has its strengths. For instance, data from Qlik can be combined with visualizations from Tableau to create a report that leverages the strengths of both tools.
Cross-Tool Collaboration: Consider a situation where an organization is working on a multi-departmental project involving both finance and marketing insights. Even if these departments use different BI tools, a unified analytics platform ensures they can collaborate without friction.
Beyond Mere Integration: The Future Vision of Unified Analytics
Unified analytics platforms are not just about integrating various tools. They envision a future where BI is seamless, efficient, and holistic, across an organisation.By bridging the gaps, these platforms are not only resolving the current challenges but are also future-proofing businesses against the evolving BI landscape.
We won’t sugar coat it. Navigating the complex world of Business Intelligence tools is no small feat. However, as we stand at the cusp of a new era in BI, it’s evident that the future is not about individual tools but how effectively they can be integrated. Unified analytics platforms are leading this change, ensuring that businesses remain agile, informed, and ready for the challenges of tomorrow.
It’s no secret that the volume of data being generated in 2023 is absolutely staggering. With this deluge of data, analytics tools have sprouted like mushrooms after a heavy rain, each promising to provide the most actionable insights to take your business to the next level. While some do, we’re looking at you Bundle, it’s probably a little surprising to hear that many companies have as many as five analytics tools in their business all working to provide these actionable insights.
This is where the main challenge presents itself. How do you manage these analytics tools without wasting time, money, and resources? Let us welcome you to the era of unified analytics.
The Fragmented State of Today’s Analytics Landscape
With so many analytics and BI tools on the market, companies often find themselves grappling with a jigsaw puzzle, attempting to piece together different tools for a cohesive view. While each tool might excel in its niche, the lack of interoperability often results in disjointed insights and a frustrated user experience.
The Power of Unified Analytics
Unified analytics platforms, like Digital Hive, address this fragmentation, serving as an integrative layer, bringing together various tools for seamless interaction. But who cares, we hear someone in the back of the room cry? You should. Here’s why:
Integrated Insights: Data sources are no longer siloed. They speak to each other, leading to richer, more holistic insights.
Optimized Costs: By unifying analytics tools, businesses can reduce overlapping tooling licences and benefit from economies of scale.
Enhanced User Experience: Users no longer need to hop between different tools. A centralized platform ensures a consistent and intuitive user journey.
Why Unified Platforms Represent the Future
Besides the benefits we’ve already mentioned, there’s a reason why more and more companies are speaking to Digital Hive about unifying their platforms. These are:
Interoperability: Unified platforms prioritize compatibility, ensuring different analytics tools can communicate and share data effectively.
Adaptability: With the rapid evolution of BI tools, platforms that offer easy integrations and can swiftly adapt to new tools are primed for future success.
Data-driven Culture: As businesses strive for a more data-driven approach, having a unified analytics platform fosters a culture of informed decision-making.
What are you waiting for?
There are 328.77 million terabytes of data created each day, and with those kinds of numbers it’s easy to see why businesses are drowning in data and disjointed insights. But with a unified analytics platform, like Digital Hive, businesses can now navigate these waters with clarity and purpose. By bridging the gaps between different analytics tools, we are not just optimizing our BI processes but paving the way for the future of analytics.
Analytics Catalogs have been listed as a Gartner Critical Capability since the 2022 version of their Critical Capabilities for Analytics and Business Intelligence Platforms. The goal of an Analytics Catalog is to unify and centralize all of a company’s analytics. There are many benefits to using this up-and-coming technology and here are the Top 5 as rated by Analytics Catalog users.
Centralizes the analytics experience for happier users.
Companies average 4 BI tools or sources of analytics. This makes it hard for users to find the information they need to make data-driven decisions. An analytics catalog allows for content to be organized for consumer consumption by job function, area, or role into a single-entry point. Users are provided with search and favoriting of all analytics from differing platforms making easier it to do their jobs. Easy to access analytics means quicker decisions based on data and more confident decisions.
Makes changing BI vendors easier by insulating users from change.
A new Analytics / Business Intelligence tool is created every day. Technologies change and the tools do too, but users are the ones that suffer. With an Analytics Catalog, users can continue to go to the same location and get the same governed content while technology teams change the underlying technology. We all know change is hard but with this technology we can avoid big bang changes and replace the regularly used content from the outgoing system piece by piece as the replacement system comes online.
Gives Analytics / BI teams and executives real usage details.
Some BI vendors make tracking analytics usage really difficult and when you have multiple BI tools, bringing that together is a “big data” project. Having a single point of entry to all your analytics via an analytics catalog creates a single source of audit data for analytics consumption regardless of the underlying BI vendor. Usage can be tracked by user, by BI platform, source and by asset just to list a few ways you can look at the data.
Usage can also come in the form of feedback. With a good Analytics Catalog solution, commenting and ranking is also available to help teams pure or improve the content they provide.
Increases analytics adoption and literacy.
Adoption starts with engaging the consumer. When consumers have multiple places to go, multiple experiences (some tailored and some not) it makes it hard. The analytics catalog creates a single place and experience for the consumers. A really good analytics catalog will let you provide different experiences for different groups of consumers. Data literacy really comes down to – does the user understand the data and the context they are consuming. There are so many fancy chart types, but you’ll find that the most used are the simplest (bar, column, pie) because they are easy to understand by the widest audience. To get to those fancier and sometimes better charts we need to explain and teach what they are showing. Lastly the ability to add context in the form of commentary is also helpful for consumers. Analytics catalogs here help by allowing grouping of analytics content together that is related. Could be a certain topic or related to a workflow. A great analytics catalog can put these assets or pieces of them together and allow for the create to add the text for explanation of what and why.
Provides pathways for Analytics Governance across the organization.
Analytics Governance is different from data governance. Data governance is rarely enforceable once in a BI tool. Analytics governance provides oversight of analytics assets, their creation, modification, and management. The data powering an asset can be governed by does not eliminate the possibility of someone hiding key elements or creating their own view by adding calculations or filters.
A good analytics catalog will allow users to differentiate content that is certified from not certified. A better analytics catalog will allow users to assist with the analytics governance by allowing them to provide feedback that can be tied to the usage data collected by the platform.
We love Analytics Catalogs and truly believe that any organization with multiple tools BI tools needs to jump on this technology for its users and to get the most of their existing and future analytics investments.
A few years ago I met Walter Isaacson, former Chairman of CNN, Editor of TIME, and author of Steve Jobs’ biography. If you
can’t tell from his pedigree, Isaacson is a great storyteller. He also wrote
about other famous innovators including Benjamin Franklin, Albert Einstein and
Leonardo Da Vinci. I only had time to ask him one question, so I made it a good
one,
“What did Jobs, Franklin, Einstein, and Da Vinci have in
common that made them such great visionaries?”
Isaacson smiled and responded, “All great innovators operate at the intersection of Art and Science.” I think Isaacson would agree this balance applies to data storytelling as well. Truly effective storytelling drives business action, and this occurs with the right mix of facts, visual presentation, and contextual narrative. Finding this balance is a challenge, but with the right tools and methodology, you can go from creating flashy dashboards to actually informing decisions.
Data Storytelling
Over the past decade, there has been a massive push for companies to leverage data. We are starting to see the Rise of Chief Data Officers. Humans are visual by nature, so we have also seen increased adoption of user-friendly visualization tools like Tableau, Qlik, Power BI, and ThoughtSpot. As the push for data democratization and access to data continues to increase, we need to ensure data is being effectively communicated and consumed – not just put into a pretty dashboard. Data Storytelling
What is Data Storytelling? Data Storytelling is translating data in an easy to understand the way to help people take action on the business. There are three main components to data storytelling: story boarding, data visualizations and data narrative.
The art of communicating using data and analytics, is still on the starting block. However, by establishing a methodology and using new technologies to support us, we can realize the full value of our data, inspire action, and transform Data Storytelling from an industry buzzword into an effective boardroom practice.
Rather than just deliver report requests, analytics teams must establish a dialogue with the business to understand the context. Context includes goals, challenges, and potential decisions that the business will make. In creating this dialogue, gaps in understanding will appear. These gaps will highlight the best questions to ask of the data. Ultimately, the answers to these questions will deliver the value business leaders have been seeking.
Using Technology for Storytelling
Once the context has been established and the right questions are being asked, analytics teams, can use technology to help communicate information with a narrative to increase understanding. We use reports and data visualization tools now. Data visualization helps us see blatant patterns, but it isn’t ideal for communicating context and situational nuances. We also shouldn’t assume interpreting a visualization is easy for everyone. With the global Data Literacy rate struggling around 24%, delivering an isolated report or visualization is risky – the information can easily be misinterpreted and lead to costly decisions.
New technology, like Digital Hive’s Enterprise Portal enables companies to easily balance the art and science of data storytelling so they can communicate and understand the entire business narrative – and ultimately make the best decisions.
By bringing together reports, visualizations, and dashboards from all of your different BI tools into a single storyboard, you can mix best-of-breed technology to deliver all of the facts. Contextually, you can incorporate video, custom messaging, presentations, and data literacy support assets to complete the narrative and inspire action.
The ideal balance of data, visualization, and narrative can now be achieved without the limitations of any one tool or technology because you can use all of your tools together seamlessly.
Conclusion
To increase the value of analytics for the business, we must find a greater balance between the art and science of data storytelling. When looking to improve the art, we must change the way analytics teams and the business communicate context. Then, we need to ask impactful questions of our data.
Finally, when delivering our findings, we should leverage technology to support us by using data visualization and data storytelling tools to communicate insight within a narrative.
*Image shows an example Digital Hive gameboard/storyboard with assets from multiple BI tools sitting side by side in a single view.
Digital Hive and Data Storytelling
Digital Hive dynamically displays content from any information system seamlessly in one unified platform – providing the easiest, most efficient, and customizable experience for the delivery and consumption of data stories on the market today. Behind the scenes, Digital Hive defends users from change-disruption, tracks analytics adoption, and reduces the IT backlog.
Gartner currently covers over 250 analytics vendors in their research. By the time you are done reading this article, I wouldn’t be surprised if there were two new vendors or tools. With the recent explosion of business intelligence and analytics tools on the market, you might find yourself drowning in a (data lake) of information and possibilities. What visualization tools should we use? Am I ready to pursue predictive analytics? Who should be using these tools? Whether your company is at the beginning of its analytics journey or operating at the bleeding edge of technology and strategy, one common theme will always be important – culture and mindset. A lack of organizational buy-in can hinder even the most well designed, thoroughly vetted analytics strategy. When it comes to data culture here are 6 essential topics to consider:
1. Data Culture is Decision Culture
Data culture may be experimental – but the objective is always to make better business decisions. Collecting data for data’s sake is useless. A great place to begin leveraging analytics is where people are already making decisions. Communicate with the leaders of specific business units and determine what critical information they use to make daily decisions. To go a step further, consolidate this information in a curated analytics experience for each department, group, or role. Once these groups begin leveraging the unique analytics relevant to their most common decisions, they will become curious what other insights they can discover. To continually improve the value of analytics, it is important to implement an effective feedback loop between business end-users and report developers. Report-rating and commentary mechanisms are critical capabilities necessary for feedback and communication between users and developers to improve the quality, scope, and impact of informational assets.
2. Data Culture and the C-Suite
There isn’t an executive you will meet today who would admit that data is not a priority in their decision-making process, but many don’t actually have a comprehensive understanding of how analytics can benefit the larger organization. Often times it is difficult for executives to define valuable problems for the analytics team to solve. Find ways for your analytics team to engage with and educate the C-Suite so that leadership understands the value behind the entire organization using analytics. A great way to demonstrate this value is by delivering a complete view of the business to your executives via a personalized business intelligence command center. Consolidate the most important KPI’s, reports, and visualizations from various tools and systems in a single pane of glass for quick, effective, executive decision making.
3. The Democratization of Data
The first step when trying to generate organizational excitement about using data for decision making is to simply get analytics in front of different groups within your organization. The informational assets available now, your reports and visualizations, might not be perfect yet – but the sooner you make them available, the sooner you can improve them. By presenting analytics to your end-users regardless of your analytics maturity, you expose them to the power of data-driven decision making, and before you know it, they will be asking for more. This is crucial in securing the organizational buy-in required for the additional investments in business intelligence that you need. One of the most effective ways to increase analytics adoption is to remove the barriers to access, and put analytics in front of your end-users through an easy to use, single point of entry for all the analytics assets you provide.
Check back after the holidays for 3 additional areas of focus when building a strong Data Culture within your organization.
If your efforts to create a rich data culture are going to be successful – identifying, recruiting, and partnering with enthusiastic data champions within the organization is an absolute must. C-Suite mission statements and company-wide initiatives that are as disruptive as digital transformation require catalysts at each level of the business to bridge long-term vision with front-line execution and adoption. This means educating and empowering middle management and the leaders of specific business units to lead the charge. The best culture catalysts will be business leaders and their ability to sell the value of analytics to their respective teams. Knowledge workers on the front-line live and breathe their daily work. Business leaders can articulate the impact data and analytics will have on daily decision making in the language of their unit and drive adoption – a process which is essential to securing top-down dedication to change. When looking at the current state of data literacy and analytics adoption within the organization you might feel that some groups are not yet advanced enough for increased access to business intelligence. You may be correct that data literacy levels are not at ideal levels, however, you can’t learn to read if you don’t have a book! Creating curated analytics experiences with varying amounts of business intelligence for different groups and roles is a great way to slowly increase access to data and drive data literacy over time. Data literacy will be crucial in every role within the next few years, so there is no better time to start than now.
5. Uniting Talent and Culture
The competition for data talent is fierce and growing, and as a result new roles and titles are emerging within the business. In the Higher Education space during 2018, CDO was more likely to mean Chief Diversity Officer than it was Chief Data Officer. However, things are changing, and they are changing fast across all industry verticals – take for example the evolution of the Data Scientist function. How does this effect culture? Given the growing need for data talent across all industries, it is now less important to hire from within your industry as you traditionally might for management, marketing, and sales roles. When it comes to emerging data talent, it is more important to find great talent that fits within the company culture of change and innovation – regardless of industry. Additionally, a diverse range of perspectives on how to extract value from data and analytics will add value to business outcomes and will help push the momentum of change within the organization.
6. Data Culture, Risk, and Ethics
The last topic we will discuss is the necessity to address risk and ethics in your data culture. Data management is increasingly important, including the ability to understand who, how, and what data people are using to make decisions. Misuse of data can institutionalize unfair biases like racism and sexism. Audit capabilities are increasingly important and valuable in understanding what data and reports are being used to make decisions.
Data culture is essential to driving the initial and continued success of BI and Analytics initiatives. No matter what stage of analytics maturity your organization is at, remember that it is important to identify daily decisions that can be influenced first, continue to educate the c-suite on the value of business intelligence, provide easy access to BI for everyone, recruit the leaders of business units to drive front-line adoption, hire great talent and include diverse perspectives, and maintain and unbiased and ethical approach to data use.
For more information on driving data decision making, read this article on BI-Modal Analytics
