*Sorry readers, we used a bad word. There may be more throughout this article, but if it encourages you to critically examine the concept of standardisation and its negative impact, then it’s worth it. Sorry, not sorry.*
Introduction
Yeah, you heard us right the first time. Standardisation in analytics tools, often hailed as the cornerstone of making technological progress, is bullsh*t. Don’t believe us? Let’s put it another way. Clinging to a single standardised analytics and bi tool can stifle creativity, hinder flexibility, and ultimately slow down progress within your business.
But that’s just one side of the argument. In this article, we’ll dive into the controversial statement, shedding light on both the pros and cons of BI standardisation, and challenging the widely held belief that standardisation is always beneficial.
The double-edged sword of standardisation
Standardisation is the process of creating and implementing technical standards based on the consensus of different parties that include firms, users, interest groups, standards organisations, and governments. It’s supposed to help maximise compatibility, interoperability, safety, repeatability, and quality.
It’s often associated with a number of benefits, including:
Increased efficiency: By creating a common framework for businesses to operate within they’re able to reduce costs and increase efficiency.
Improved quality: By ensuring that services meet certain standards they can improve the quality of those products and services.
Increased safety: As above but replace quality standards for safety.
Increased compatibility: Standardisation can help to increase compatibility between products and services, making it easier for businesses to work together.
Reduced spend: Removing expensive and overlapping tooling licences and removing the need for multiple ABI teams (one per tool) and overlapping infrastructure (per tool). Don’t forget better rates that come with increasing buying power with the chosen Analytics and BI vendor!
Sounds incredible, right? Who wouldn’t jump at the chance to be more efficient and cost effective while improving quality and safety for products and services. But as the subtitle suggests, this is a double-edged sword, and sadly this blade is pretty sharp.
You see, standardisation can also backfire for companies who embrace it. For example, it can:
Lead to vendor lock-in: When a company standardises a particular Analytics and BI platform, it becomes more dependent on that vendor. Making it more difficult and expensive to switch to a different platform in the future.
Reduce flexibility: Standardisation can reduce the flexibility of an analytics team. This is because a single vendor will do some things well and others not so well. The things it doesn’t do well lead to rigid solutions around the limitations.
Stifle innovation: Standardisation can stifle innovation by discouraging developers from developing new and unique analytics applications. This is because developers may be reluctant to invest time and effort in developing applications that are not compatible with standardised platforms.
Opportunity cost: Migrating existing content from other Analytics and BI tools is time consuming and costly. Typically, everything gets moved (without knowing the value), which means new projects aren’t being done and you’re missing opportunities wasting time on stuff that isn’t needed.
Not so great now, is it?
Standardisation Simplifies Interoperability
Another reason that people gravitate toward analytics standardisation is due to the way it simplifies interoperability or removes the need for interoperation entirely. The allure of everything working together seamlessly, fostering compatibility, and reducing friction in user experience is too enticing to miss out on. I mean, imagine a world where every manufacturer had a different design for electric sockets or USB ports – chaos would ensue – we’re looking at you Apple!
But does that mean you have to succumb to the other negatives we discussed? There may be a better way.
At Digital Hive, we like companies to have freedom within their analytics tech stack, utilising tools and services that tick every box based on need, not just a few because the others won’t play nicely together.
By layering Digital Hive over your analytics tech stack, you get the benefits of standardisation without the negatives that accompany it. Instead, you get to keep the ABI tools and services that work for your organisation and your individual business unit needs, while adding in a branded front end that is as simple or in depth as you need it to be.
Imagine a place where all your analytics assets live, easily accessible without having to reinvent the wheel on how it’s accessed. Now imagine having to standardise that content to fit a new product just because it plays nice with the flavour of the week tech that no one wants, but it’s part of the package you just bought. Got to get your money’s worth, right?
Stop Fitting Square Pegs in Round Holes
Okay, the title is a bit provocative, but you get the point. While standardisation offers undeniable benefits, it’s not a panacea. It can, and does, block innovation, reduce flexibility, and stifle competition.
The key is to strike a balance. By using Digital Hive to collate ABI software into one easily accessible front end, you can begin fostering an environment that encourages usage, improves productivity of users and power users, adapts to change and helps BI teams prioritize work and understand value. We can enjoy the benefits of standardisation without falling into its potential pitfalls. After all, in the dynamic world of technology, adaptability, speed and balance are the keys to success.
Thus, it’s not that standardisation that is bullsh*t; rather, it’s that blind adherence to standardisation, without considering its potential drawbacks and the need for balance, can lead us down a problematic path. By recognising this, we can navigate the complex landscape of technology with a more nuanced understanding and a greater potential for progress.
For more information about Digital Hive and how we can work with you to achieve amazing results, contact us today.
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
Digital Hive has been named a 2020 Gartner Cool Vendor in Analytics and Data Science
With the average organization using 3.8 different BI solutions, and the number of different business roles wanting to analyze the data increasing, it’s critical that businesses make it easy for users to leverage, share and scale the analytics value from different systems that have been generated before.
According to Gartner’s report, published May 7th, 2020:
“Organizations are struggling to manage analytics content from different tools. This hinders the ability to share and scale the use of analytics, and limits adoption as users fail to find and compile the insights that have been generated before.”
Garter recommend that one way this can be achieved is by
“establishing an easily accessible portal that has single access to the analytics content built by multiple existing analytics solutions.”
Gartner’s definition of a Cool Vendor is “a small company offering a technology or service that is: innovative — enables users to do things they couldn’t do before, impactful — has or will have a business impact — not just technology for its own sake, intriguing — has caught Gartner’s interest during the past six months.”
Why is Digital Hive Cool?
Digital Hive’s technology consolidates key information assets across an entire organization in one convenient and digestible place, giving users real-time access to the relevant information they contain through a single point of entry.
Digital Hive (formally known as Theia) connects to analytics and BI tools platforms such as ThoughtSpot, Tableau, Qlik, IBM Cognos as well as standard document systems such as Google Drive, SharePoint, Box and social media platforms.
Digital Hive’s analytics catalog addresses a real pain point impacting organizations using multiple analytics and BI tools — giving business users a single point of access and thereby providing visibility, governance and control.
Click hereto read Gartner’s full report – link off to Gartner.
Gartner Disclaimer: The GARTNER COOL VENDOR badge is a trademark and service mark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
As companies race to find value in their data and improve the customer experience, many have overlooked an obvious value-add: providing analytics for the customer. Client-facing analytics differentiates companies from the competition, helping increase market share, retention, and even direct revenue if packaged and productized.
Why aren’t all companies doing this already? The data is there, and the reporting, dashboards, and visualizations are plenty.
The Challenge
One of the most difficult aspects of trying to provide analytics to customers is delivering a complete, appealing product. With analytics coming from a variety of internal business intelligence tools, packaging all of this information is far from easy. Both internal and external, users desire a seamless analytics experience. If bringing all of this content together into a single user experience wasn’t challenging enough, each experience needs to be tailored for various audiences.
On top of wrangling content, curating different experiences, and creating a pretty product – facilitating understanding is also a challenge. Chief Data Officers are tasked with fostering data culture, increasing BI adoption, and improving data literacy. However, these initiatives shouldn’t be limited to the internal organization. Companies need to extend this concentration to clients and partners as well.
The Ideal Customer Analytics Portal
Given the needs and challenges discussed above, let’s describe the ideal external analytics portal for clients and partners:
In summary, companies should aim to deliver analytics both internally and externally to clients and partners to maximize the value of data and grow together with strong data-informed relationships using an analytics portal.
The ideal analytics portal integrates reports and visualizations from all of your different BI tools, allows for the curation of content for different groups including data literacy support, and provides an attractive and easy to use experience.
Why Act Now?
If you provide your clients with informative analytics to help them grow their businesses, your business will become an irreplaceable source of value. If you DON’T provide your clients and partners with analytics, someone else will.
The demand for analytics is continually increasing as companies use data to drive decision making. If you are not providing clients with informative analytics to help them grow their business, how are you ensuring that your service will not become an irreplaceable source of value?
Offer your clients a service that goes above and beyond the competition. Replace any frustrations by giving them full autonomy over their analytics environment.
How to create a Customer Portal with Digital Hive
Read more about our enterprise portal solutions, or get in touch, if you want to chat about customer analytics portals – we can show you how simple it is to get set up!
Book a demo with a member of the team. See the full Digital Hive experience as well as some of the branded customer portals we’ve created.
