Top Trends in Analytics 2022

Top Trends in Analytics 2022

Centralize, Understand and Trust your Data

Data and analytics continue to deliver increased value to organizations in many ways, from boosting data-focused product offerings to driving those all-important fact-based decisions.

As industries skyrocket, there are several trends that are quickly gaining traction. So, what will be shaping the world of data and analytics for 2022 and where should your top priorities be?

 

Driving Data Literacy

According to Gartner* by 2023 data literacy will become essential in driving business value. As it is fast becoming a critical skill regardless of the role within an organization can you really afford not to address those chronically poor data literacy rates in 2022?

As companies continue the sharp shift towards data-driven decision-making and smart analytics, the ability to understand data and use it as a collaborative tool throughout your organization is vital for success. Organizations need to empower frontline users (not just analysts and data scientists) with the knowledge, skills and technology to make informed decisions quickly and get ahead of the competition.

But it doesn’t stop with merely understanding the data in front of you, users need to become truly data savvy and be able to tell accurate stories around the data. This is where the skillset of the data scientist historically met data visualization and creativity. But, in today’s world we need to arm all users with the tools they need to visualize data effectively, so that insights can be pieced into accurate data stories.  It is no longer just the domain of data specialists to make agile, data-driven decisions in real-time – it’s everyone.

Data Literacy can no longer sit on the ‘nice to have’ list. It needs to become a key priority for every organization. The reality is if you aren’t prioritizing it your competitors will be. And, more crucially, they will be reaching those data-driven conclusions before you’re even off the starting blocks!

Question:
How can you start to improve your data literacy levels when you’ve got data and analytics spread over multiple sources and people working in data silos? How can employees extract insights from multiple sources, analyze and understand them, add narrative and context before communicating them?

Answer:

One way to drastically improve the data literacy rates starts with an enterprise portal. Organizations need to look at consolidating analytical systems and centralizing data to provide users with an easier and quicker way to access information from multiple analytics and information systems. Ensuring ALL key insights (not just half or what they only know exists) can be pieced together in a single storyboard to tell a data story.

Learn more.

 

Centralization is king

We all know that there isn’t one BI or analytics tool to fit every use case. So, if we can’t standardize onto one tool to avoid data sprawl could centralization be the ‘Golden Goose’ you’re looking for? By centralizing access to all data, analytics and information through a master repository you could rid yourself of data silos once and for all and start driving those all-important data-literacy rates upwards in an instant.

Enterprise portals are a pragmatic way to centralize multiple analytics, BI and information assets into a single interface making it easier for end-users to consume the data with modern and app-like user experiences (learn more). Through this modern technology, users are shielded from the underlying complexities of multiple systems and have the information they need to do their job right at their fingertips. Meaning less time searching for information, more time understanding it and driving those data-driven decisions.

Unifying data and analytics into a centralized hub has other benefits. For example, when data is scattered across multiple sources, it can be difficult to ensure the accuracy and trust in data which can diminish the reliability of findings. This could result in decisions being made that are based on inaccurate or biased assumptions.

By centralizing your data and analytics into a central Catalog you can provide access to a myriad of sources where the data can be examined and dissected but with the trust that you are still providing end-users with a single version of the truth. Centralization gives you full visibility of your data and is key to building trust in the insights being presented.

Building Data Trust

 Trust in data is crucial for insight-driven decision-making. As more companies race to unlock the potential of artificial intelligence, machine learning, and automated analytics creating trust in data sources is becoming highly important. The need for accurate and centralized audit and governance capabilities across entire enterprises will be paramount in building this trust. If you can see it, you can believe it.

 Through an enterprise portal you can easily build and administer data policies that ensure data accuracy is maintained. Helping to build out trust throughout your data and allow teams to become more autonomous and self-sufficient in line with the expectations of any multi-generational workforce.

  

Self-service culture

 Self-service is on the rise for all end-users, but for data analytics it’s about more than just the technology. Building a culture around your data and how your users can leverage it to drive organizations forward will be invaluable over the coming months.

 In 2022 we’ll see more companies than ever adopting self-service analytics tools that allow non-technical users to securely access and glean insights from data. This means more end-user involvement in all stages of the data journey, making it faster and easier to extract, harmonize and visualize insights driving better and faster business decisions.

 With a self-service portal, users can easily create and share their own visualizations, dashboards and reports without the need for a technical background – without any intervention from data and IT teams – meaning they have more time to focus on the stuff that really matters. This enables easier access and collaboration for all users leading to the end of data silos across your enterprise. Watch and learn 

 

Wrapping it up

 It’s no coincidence that each of the above links back to the need for us all to become more data-driven. It’s no longer an ideal, but a necessity in the modern business world and driving data literacy needs to be top of your priority list for 2022.

Companies need to encourage and empower users across their organization to become data literate and put in place the infrastructure to make this possible.

Centralizing all analytics and data into a single interface will be key to moving your business towards being truly data driven. As the age of automated analytics is upon us it will be imperative to provide that single version of the truth across your entire data portfolio.

If you build trust in your data, then improved adoption and data literacy rates will follow. The key for 2022 will be to empower all users, not just those with a technical skill set, to understand your data and tell accurate informed stories based on it.

If you’d like to know how an enterprise portal could help you to start your journey and address the all-important 2022 priorities get in touch we’d love to chat. Get in touch

 

*Gartner reference 

 

Data Storytelling: An Intersection of Art & Science

Data Storytelling: An Intersection of Art & Science

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.

Capturing Business Context

All BI and Analytics initiatives should aim to do the following: make money, save money, or protect against risk. However, only 20% of analytics insights are predicted to produce a business outcome through 2022 according to Gartner. To unlock greater value, analytics teams and business leaders must radically change the way they communicate.

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.

Analytics from different BI systems side by side
*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.

Click here to download our e-book 7 Steps to Drive Data Literacy‘ or book a quick 30 min 1:1 demo with a Digital Hive expert!

Data Culture Matters (part 1)

Data Culture Matters (part 1)

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.

 

Data Culture Matters (part 1)

Data Culture Matters (part 2)

4. Data Champions and Culture Catalysts

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

Does Gartner’s “Cool Vendor” Report Mean Anything?

Does Gartner’s “Cool Vendor” Report Mean Anything?

Every year, Gartner, one of the world’s leading IT research companies, publishes a Cool Vendor Report highlighting new technologies in Analytics and Data Science. This year, Digital Hive (formally known as Theia) received this recognition. Previous Gartner Cool Vendors include Tableau, Snowflake, and Looker just to name a few. Many CDO’s and CTO’s use Gartner to help successfully guide their IT strategy and technology purchasing decisions.

So, does being named a Gartner Cool Vendor mean anything to Digital HiveYES!

The Intersection of Need and Solution

Gartner analysts are some of the most informed middle-men in the world. Working with both technology vendors and enterprise data leaders, they sit at an incredibly valuable intersection of information. It’s at this intersection that Gartner is able to provide value, having 1000’s of conversations a year on both sides of the fence, identifying emerging challenges and the technologies that will provide a solution. 

Validation of Enterprise Need

For Digital Hive, being named a Gartner Cool Vendor is a validation that the use of many BI & Analytics tools within large enterprises is creating real problems for end-users with BI Adoption, Data Literacy, and meaningful ROI. Companies are now in a full-sprint race to go digital, maximize the value of their data, and provide more meaningful experiences. Without frictionless access to information, meaningful data literacy education, and a push for greater user adoption, many companies are going to see the millions they invested in data & analytics go down the drain.

Even more, we now have the validation that a multi-tool tech stack is NOT going away. Co-existence is needed. If consolidating to a single BI tool is on your roadmap, be forewarned it’s a long, expensive, and ever-changing journey. Every year innovations in data & analytics produce new tools and vendors, making it necessary for companies to keep pace and maintain a competitive edge.

A Rise in Vendor Competition

For many, competition in the marketplace is a threat. For Digital Hive, seeing the recent increase in competition indicates that there IS a marketplace for our Analytic Hub solution and that we are providing much-needed value.

We welcome new competitors as we work towards a common goal –  increase awareness of the value in Analytics Hubs, Analytics Catalogs, and data literacy support.

Why Digital Hive is the Best Analytics Hub Solution 

That being said, Digital Hive was the first Analytics Hub solution to identify and solve the market need of bringing enterprise BI together. The first-mover advantage has allowed us to evolve and innovate more quickly than our competitors and provide increased value to our clients as their needs change.

Digital Hive is uniquely differentiated as the only true technology-agnostic analytics hub that allows users to interact with the full-functionality of different reports and dashboards seamlessly within the experience.

Most of the competition in our space only provides a functional analytics catalog (or is yet another BI tool in disguise). Digital Hive is the only true Analytics Hub. In addition to an Analytics Catalog Digital Hive enables end-users to create interactive Data Storyboards using reports from different tools, and contextualize information with presentations, video, RSS feeds, and custom messaging. 

This means that clients can unlock the full power of Data Storytelling and Data Literacy support. Every department, persona, partner, or client receives relevant and tailored information supported by context.

Only Digital Hive can do that.

Learn how

How You Can Use Digital Hive Today

Digital Hive has clients in every vertical, all around the world – Financial Services, Healthcare, Higher Education, Manufacturing, Retail, and more. With out-of-the-box connectors to 18 major information systems and a Custom API connector, any business can bring their choice of BI & Analytics tools together and benefit with Digital Hive.

Want to learn more? Book a 30 min demo today!

Gartner Cool Vendors 2020 Announced

Gartner Cool Vendors 2020 Announced

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 here to read Gartner’s full report – link off to Gartner.


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