Digital Transformation — How Analytics Is Driving the Change
Businesses have always had to contend with changes in technology. But today, change is happening at a much faster pace as companies use new capabilities and rapidly emerging technology to improve their products and services. The aggressive rate of change requires constant innovation. That’s digital transformation.
Digital transformation, as defined by Gartner, is anything from IT modernization (for example, cloud computing) to digital optimization to the invention of new digital business models.
To bring organizational and business success, digital transformation must involve lasting cultural and technological change. This signifies a long-term strategy for the company, not a short-term tactical solution. The goal should be to become at ease with change before the market demands it. Because the market will demand it. The threat of disruption will always be present in every industry, every vertical market. And it’s never over.
In this white paper, we will examine how data leads to digital transformation, the challenges modern organizations face with their analytics, and why they are turning toward data to make business decisions.
- What digital transformation is
- What data-driven companies are doing with their data
- How digital transformation increasingly involves migration to the cloud
- The role of embedded analytics in digital transformation
- How to monetize data with digital transformation and drive revenue
- How Sisense makes a difference: case studies
Chapter 1: Digital transformation and data explained
As the volume of data keeps growing, businesses need to understand its value. Dresner reported that nearly 97% of respondents in its Big Data Analytics Market Study consider big data either important or critical to their businesses. To get ahead, and stay there, now requires the power of data to drive decision-making.
This makes data an essential element of digital transformation, and it explains why 98% of firms invest in big data and AI initiatives. Nevertheless, 73% of firms consider it to be an ongoing challenge, and only 38% feel they have built data-driven organizations to date. This may be related to leadership issues, such as not having a CDO to take control of the transformation. The role either remains vacant, or, as in 72% of firms, it is unsettled, owing to the rapid and disruptive changes that big data has caused in recent years.
Now is the time to consider why, then how, digital transformations supercharge businesses and the critical role that C-level executives and product teams play in making that happen.
There’s no standard digital transformation road map or playbook that can be used universally. This is because every business and organization is starting from a different place on its journey. In the chapters below, we outline three waves that will help you identify where you are in your own digital transformation and take steps to continue on the journey. Your organization may need to adjust existing software or work on development methodologies, business processes, and personnel responsibilities — or you might already be in a good position and just need to add new functions.
With that in mind, we can narrow down most digital transformations to a few key elements they typically encompass: putting analytics in the hands of everyone throughout the company, building analytics into the product, and monetizing data.
“A digital transformation isn’t just about streamlining your business so that you can do what you were doing before only better. It’s about imagining a bold new direction for your business and having a clear vision for what you’re building.”— Guy Levy-Yurista, Chief Strategy Officer at Sisense
Businesses need their digital transformation to flow throughout the company while enabling everyone to adopt the practices voluntarily. Every company must ensure that resistance to change is overcome by starting with its people. By giving them a voice to discuss and explore this new digital reality (through culture, collaboration, and open processes, for example), you can find new ideas that drive innovation. This can be done with small groups, and then you can escalate the process to the entire organization.
Another essential consideration is where the innovation of the company should take place. Consider putting this obligation in a team that can quickly deliver on new ideas and not in a mature business unit. An agile, fast-reacting department that reports to the chief innovation officer is a good place to start.
It is also vital to have a clear vision of what you want your digital transformation to achieve, so that during the process, you can make sure you’re on the right track and measure this progress against key performance indicators (KPIs) that you have identified as indications of success.
In a blog post for Sisense, Jack Cieslak gave this example for making better use of data: “Every company knows it needs to digitally transform in order to survive and excel in the modern era. However, many organizations fail to define their goals for this process before they start and predictably encounter obstacles or outright failures instead of paving a path for future success.”
Business goals should be defined at the very beginning of the digital transformation. You can start by answering this question: What is the organization explicitly looking to solve or improve? Understanding the details is key to defining a long-term strategy and avoiding costly pitfalls.
From there, rather than trying to accumulate and house the company’s entire dataset, the digital transformation team should identify the specific, actionable insights and associated data needed to solve (and measure) agreed-upon outcomes.
The right analytics and BI platform can help immensely with this process and will lay the foundation for the future of your data-driven business.
Chapter 2: Data-driven companies and digital transformation
Every company is a data company. Companies produce and collect more data than ever before, and that amount will only keep increasing. And as progressive companies bring more and more data insights into their customers’ world, there will be increasing pressure for competitors to do the same.
Your organization is already sitting on valuable data sources. Here are some familiar sources where you can start looking for useful data:
- Customer relationship management (CRM) and enterprise resource planning systems for transactional data/purchase histories
- CRM, customer support system, internal product analytics for data about service/product use
- Google Analytics, Google Search Control, and additional marketing technology platforms for online behavior data
Once you discover all your data sources, you can categorize your digital transformation into three waves: 1) understanding your data, 2) using self-service BI, and 3) driving outcomes. Each wave builds on top of the previous one and transitions the business into a data company. The waves enable you to become familiar with your data, start to discover insights, and eventually take action on the insights.
The first wave of your digital transformation is about making sense of your data. This may require compiling data into data warehouses and leveraging a central BI team to create reports mostly for internal use.
In this first wave, reporting is used as a rearview mirror where the primary objective is to understand the existing business. Your business users should become increasingly curious and frustrated at the inability of these central BI teams to keep up with their growing demand for answers.
In the second wave of digital transformation, you should see the rise of self-service BI tools. The goal of self-service BI is to make it easy for your business users to answer as many questions as possible on their own without going back to the overloaded IT teams. These tools make it easier to combine data sources and create content using simple menus and drag-and-drop user interfaces.
Even more critical, self-service BI has made it possible to slice and dice the same data in different ways so that your users can answer even more questions without having to go back to IT for a new report or new dataset. That’s where dashboards come in. Like the dashboard instruments in your car, these BI dashboards help the business users understand what is happening at the moment through KPIs.
Business reporting and self-service BI continue to be extremely relevant today in the first and second waves of digital transformation. But in both of these cases, the types of problems that can be solved are limited — and therefore, so is the impact.
At the core, these two waves are focused on increased transparency and operational efficiency. However, in today’s digital economy, that is no longer enough. What you need are actionable insights.
Getting actionable insights is the holy grail of digital transformation. But first, we must better understand what this means, by defining non-insightful versus insightful findings.
Non-insightful data is everything you knew before you found this data. It is an issue that management was already aware of. For example, customer churn is higher at the end of the year or when their license is about to expire.
Insightful data is everything that was hidden inside the data. Once you dig up these insights, you will begin to contradict what you thought was true, confirm your suspicions, or quantify their importance. For example, if the analysis reveals that 65% of customers churn at the end of the year, but 90% of customers churn when their license is about to end, those insights can be used to strategize customer churn going forward.
Insight is the understanding of a specific cause and effect within a particular context. It’s a finding that contradicts your knowledge or confirms or denies your suspicion.
Organizations that begin to base their business and operational decisions on actionable insights completely change the way they operate. They are focused on using these insights to make decisions, drive action, and create better partnerships with vendors and customers, both inside and outside their organizations.
“Soon, data itself will become a primary product for nearly every business, and data analytics (some use the loose term ‘AI’) will form the core of every company’s business model. Nearly every product on the market will be forced down one of two paths, becoming either ‘smart’ (i.e., analytics- and data-driven) or obsolete. This may sound extreme, but I truly believe it and have seen it happen firsthand.”— Amir Orad, CEO of Sisense, in Forbes, February 14, 2020
Adding supplementary sources of data
Once you have discovered all the internal data sources and started to find actionable insights, it is time to consider data from outside sources to help you expand your business and identify strategic partners.
There are plenty of places that sell data. One example is Data StreamX (Quadrant). This organization was founded in 2014, and its mission is to escalate data access worldwide by merging the buyers and vendors of data onto one simple-to-access platform. Data StreamX proudly runs the global marketplace for commercial data.
Then there is open data. Open data is important because the world has grown increasingly data-driven. Data can help fight global problems such as disease, crime, or famine. World Bank Open Data is a great example of a bank of open data sources that can be sourced. Another source that became popular during COVID-19 is the World Health Organization (WHO) open data repository.
Acoer worked with Sisense to develop a coronavirus tracker. The tracker takes public data made available through different authoritative sources (the Centers for Disease Control and Prevention, WHO, Institute for Health Metrics and Evaluation, New York Times, and Google Trends so far) and displays it in aggregate for a global perspective. It allows for simple and visual filtering and dynamic changes based on values selected, and it enables you to download selected data, either as images or CSV files for later analysis
Interfolio works with Sisense for internal analytic solutions that connect to a growing list of third-party applications like QuickBooks, Salesforce, and Pendo. With a cloud data strategy, Interfolio invested in Snowflake for data warehousing and chose Sisense for the analytics. This gave the organization the flexibility to prepare, query, and manage data within Snowflake using both code and a web-based, visual data modeling experience. And with the option to use both live and in-memory data options, Interfolio’s ability to derive greater insights from its data would be limitless.
With all of its major third-party data sources integrated into its analytics workflow, Interfolio can now begin to integrate external datasets that will allow institutions to understand where they stand in terms of Equal Employment Opportunity guidelines and compare themselves against industry benchmarks.
Chapter 3: Digital transformation and the cloud
Digital transformation now requires a shift to cloud analytics, or a hybrid (combined cloud and on-premises) approach, to handle the volume, expedite the time to insights, and enable a wide variety of teams to get the specific insights they need from large and complex datasets.
Legacy on-premises architectures are limited in terms of capacity and are often too slow. Plus, these traditional tools rely heavily on IT teams to crunch the data and offer it to users in a way that enables them to derive insights. For a mature enterprise, a digital transformation is a multiyear process that includes a cloud migration to help make data more accessible and portable.
So, switching to the cloud frees organizations from the limitations of on-premises solutions. It is future-proof and can grow as requirements change. That is reflected in typical payment plans that are usually pay-as-you-go, which means organizations are not boxed into a usage agreement that limits capacity.
Organizations’ data needs can change rapidly, depending on a wide range of conditions that can suddenly shift. We only have to consider how the COVID-19 pandemic drastically affected nearly every company and organization, almost overnight. And these changes continue to occur as organizations respond, sometimes daily, to new directives from governments and health authorities on how they can operate in these challenging conditions.
Companies are now looking to spend on new cloud technologies that allow them to better analyze and monetize their data, according to The Wall Street Journal. The Journal states that companies are diverting capital spending from IT hardware to cloud services and AI. Also, most companies are putting a premium on technology that supports agility.
“As a result of COVID-19, there’s a premium on agility,” Crawford Del Prete, IDC’s CEO and former chief researcher, told the Journal. “And the cloud and associated services can give enterprises a high degree of agility.”
Survey shows new data use cases leading to cloud migration
In April 2020, Sisense commissioned The State of BI & Analytics report to understand how companies are coping with COVID-19. Respondents across all industries who reported using data for the following new purposes are also the most likely to consider re-platforming (moving to the cloud) as a result of COVID-19:
Our research shows that since the start of the pandemic, customers and prospects who reported using data for these new purposes are also the most likely to consider moving to the cloud.
- Embedding analytics: 35% who started embedding analytics into their products are likely or very likely to move to the cloud
- Cutting costs: 31% who are using analytics to cut costs are likely or very likely to move to the cloud
- Sales funnel optimization: 31% who started tracking their sales funnel with analytics are likely or very likely to move to the cloud
Among infrastructure decision-makers whose firms have implemented public cloud or plan to, 38% say that on-demand capacity and scalability was an important reason for adopting a public cloud strategy. With cloud services typically delivered on-demand in a pay-as-you-go model, there are few or no fixed costs or commitments, and data storage is designed to precisely meet users’ requirements.
Cloud services can scale up or scale down dynamically, depending on an organization’s needs. This means users are never locked into an agreement with a provider that might not allow them enough capacity or might leave them paying for capacity they are not using. Hence, they avoid unnecessary expenditure on under-utilized resources.
AWS, Google, Microsoft Azure, and Snowflake are leading examples of public cloud data warehouses. With such a variety of sources, there is no risk of being locked into one particular cloud services vendor, meaning you can keep all the options open for expansion and growth.
The marketplace has shown that moving to the cloud can add value to data analysis, boost growth, and reinforce the competitive edge of products and businesses.
Chapter 4: Using embedded analytics for digital transformation
When creating your organization’s digital transformation strategy, you typically focus on gathering, managing, analyzing, and visualizing data. This is what will help you streamline your processes and enhance your business. However, for a true digital transformation, you need to think about what else you can do with your data. For this, you need to think differently about your data and consider embedded analytics.
While data adds value internally, embedded analytics gives you the extra capability to create value for your customers by turning data into data products. Adding reporting, data visualization, and analytics tools to existing applications, or developing new applications with these features, makes usage data just as accessible for customers and consumers. With embedded analytics at their fingertips, everyone can make data-driven decisions. This promise of adding value increases the stickiness and differentiation of companies’ products and services, escalates user adoption, and creates new revenue streams.
“If you look at statistics on the different usage and adoption of analytics, it tends to be pretty low in general within organizations, and I truly believe that’s for one reason — most BI platforms assume people will unnaturally leave their everyday workflows and processes and look at a dashboard and then come back. With our API-driven platform and approach, we can bring analytics to the salesperson that spends their entire day in whatever sales platform or CRM platform they use, and, for someone like me that’s always on the go, send it to my cellphone. By continuing to innovate in that area, I think we can start to see adoption go up and really start seeing organizations make data-driven decisions, and then, of course, as an end result, have better business outcomes.”— Ashley Kramer, Chief Product Officer at Sisense, in TechTarget, July 2, 2020
Essentially, embedded analytics takes individual visualizations, or the person’s individual KPIs, and embeds them directly in their workflow. So as the person uses their favorite tool, they get their own KPIs right in front of them — the right information at the right time to the right person.
Embedded analytics increases stickiness for the company and engagement for the user. Think about offering a basic level of analytics at first and then adding augmented analytics for a premium or fee.
In the Hive project management platform, users can check how long it takes them to finish their tasks, identify bottlenecks, and gauge efficiency using Sisense embedded into the platform. Recurring Insights, which makes a platform used by a large number of charitable organizations, built embedded analytics into its software to help its users drive strategic, predictive decisions and get the most out of every fundraising campaign.
It’s also possible to embed analytics into Alexa, for instance. By asking Alexa a question, she will answer back. This is one way to embed analytics into people’s lives. Simply ask, “Alexa, what is my total revenue?” And she answers.
For enterprises, embedded analytics is a way to mature the BI offering. Start by putting developers into the mix. They can integrate analytics into their app as a first step. Then they can take embedded BI even further, putting it into employee portals and systems such as Salesforce, and even offering analytics to customers and partners.
Embedding analytics into your products with the help of developers should be part of your digital transformation strategy. Developers can make insights more accessible for everyone, regardless of technical aptitude. They become key players in improving decision-making throughout the entire organization. Bringing developers into the digital transformation strategy will help you see ROI even sooner. In addition, by creating new revenue opportunities with stickier, customer-facing apps with embedded analytics, the product team is creating another way to help accelerate your company’s data monetization.
Chapter 5: Digital transformation can lead to data monetization
According to McKinsey & Co., data monetization is the process of using data to increase revenue.
Through digital transformation, companies can use data to build profitable new products, new lines of business, and even entirely new industries. As your company moves to be more data-driven, figuring out how to efficiently monetize your data insights will be the crowning glory.
The process of monetizing data is not easy. To do it well, organizations need to embrace data analytics as a building block. To build and execute a successful data monetization strategy, you need two essential assets. It is important to remember that every company today, no matter its industry or market position, has these two tools at its disposal:
- Domain expertise in the industry: Look toward your employees to add the expertise in the domain in which you operate
- Internal and external data: Find the internal data that is managed for your organization and the external data that you create and manage for your customer
By combining these two assets with an analytics platform, the right innovation framework, and the right technology, you can create a new revenue stream for your company and value for your customers.
It’s important to think about what type of data you need for data monetization. There are two types of data that every company has:
- Data your company generates and owns
- Data your customers generate and own. You are only managing this data for your customer
Now is the time to make sure that, no matter what you plan to do with the data, you are legally within your rights to use it. Consult your company’s legal department, and refer to the user agreement contracts for proper wording to allow you to use the data you have.
The third type of data your company should consider is third-party data, which can be used to add context to datasets.
Let’s take a healthcare example. A hospital gathers data on patients, but this amounts to only 2% of the total data around each patient. To augment the context of data, you will need to add data that is created elsewhere, such as eating habits, physical exercise, etc. By adding external data to what you have, you can build a complete picture and extract better insights for the patient.
What’s important to remember is that organizations already possess highly valuable knowledge, expertise, and data. Every function, operation, and transaction within an organization generates data that can hold the key to revenue-raising insights.
Success Story: Erea Consulting sells data to customers, and customers sell their data to suppliers
The retail industry in Latin America and Europe suffered from a lack of information sharing. What it did share was not timely and became quickly outdated, as it was shared in an Excel file just once a month. Erea Consulting solved this significant gap by offering real-time data among hundreds of suppliers and hundreds of stores.
Erea offers its customers a platform that makes commercial insights immediately available to the entire retail supply chain in a universal container. In turn, these customers provide their retail data to their individual customers, enabling everyone in the supply chain, suppliers and retailers, to access up-to-date data for prompt decision-making.
“We wanted to replace the dull and ineffective data-sharing processes that retailers and [fast-moving consumer goods] companies were accustomed to. The market desperately needed access to a data and analytics platform that could revolutionize the way retailers and suppliers interact on a daily basis.”
— Michael Corcuera, CEO of Erea Consulting
The EreaBI Analytics Platform for Suppliers allows supply teams to track and better plan their inventories at an SKU level per store. Stock can be transferred from one store to another, and customers experience fewer out-of-stock items on their supermarket trips. Additionally, suppliers can monitor the performance of ongoing store promotions, get visibility on brand switching events, see how their competitor’s brands and products react, and even have visibility on discounts to maintain market share.
Retail stores are able to sell this data back to suppliers. Unisuper generates close to $2 million annually using the EreaBI platform. As a supermarket chain, it sells data back to suppliers via its white-labeled UNIBI platform.
“It took five months to start monetizing our data. We don’t make it mandatory for our suppliers, but they know that if they don’t, they are at a disadvantage to competing brands at our supermarkets. Since implementing the solution, our revenue stream from selling data has gone up 75%. We are getting close to 0.6% of the sell-in from suppliers. And the best part is: Monetization is straightforward. Suppliers want the platform, and our internal teams want it too.”
Chapter 6: How Sisense has made a difference
GE digital transformation: Enhancing access to data, accelerating time to insight, and boosting cost-effectiveness
The data and analytics team at GE Cyber Security is responsible for driving the company’s digital transformation. The team created a self-service analytics environment using Sisense and Amazon Redshift. This enables users to gain access to faster, more accurate, and consistent data throughout the organization. This method also eliminates the bottlenecks caused when taking the traditional approach of waiting for BI analysts to explore the data and produce dashboards. The time it takes to generate dashboards has been slashed from weeks to minutes.
The first step involved migrating data workloads from legacy on-premises infrastructure to the cloud. The company’s data and analytics team now hosts the BI environments on AWS and has automated its analytics deployment.
Since migrating to the cloud, teams have seen benefits including saving resources, time, and money. The team reports that 80% of manual operational tasks were eliminated through automated monitoring and remediation. Environment deployment and upgrades now take four hours instead of two days. Data storage costs have fallen by about 83%, and the cost of infrastructure to host BI software has decreased by 54%.
Trax digital transformation: Helping retailers make smarter decisions
Trax’s computer vision platform helps international retail brands like Coca-Cola, Nestlé, and Heineken understand how their products look, perform, and compete on the shelf in real time. Trax originally had an in-house solution that customers did not use because it did not provide the ease of use and flexibility they needed. Any change a customer requested required the product team and multiple cycles of development in order to implement. Trax looked for a solution to reduce impact on these two teams while delivering more intuitive options to customers.
Trax uses the Sisense embedded analytics platform with Google BigQuery for better performance and ease of use. Both internal users and Trax customers experience the benefits of this solution, with 70% of the data model replicable across all accounts. Trax’s customers can build their own data experiences within a single platform, which means the Trax team saves considerable implementation time.
“With our previous solution, we were paying for a huge server — the costs for both the infrastructure and the application were enormous. Once we moved to Sisense with BigQuery, this cost reduced dramatically. Now, we pay as we go and scale as our customer base — and our data — expands.”— Ilan Pinto, Director of Software Development at Trax
GeriMedica digital transformation: A philosophical data revolution in the healthcare sector
GeriMedica is a Dutch-based Saas provider that delivers software and service for healthcare professionals operating in the elderly care sector.
To Hamza Jap-Tjong, Owner and CEO at GeriMedica Inzicht, it was clear that multidisciplinary electronic medical records could deliver more insights that have a meaningful impact on the level of care patients receive. He knew his customers were spending hours trying to extract insights from exported reports, and the product’s current dashboards were not useful.
GeriMedica Inzicht used the Sisense embedded analytics platform to give actionable insights that improve patient care paths. Customers enjoyed increased productivity and outcomes across all aspects of care with a unified view of all possible care paths. For potential customers, a quick demo allows them to see value upfront from their data and understand how simple it is to validate top-priority insights.
Within the first six weeks of embedding Sisense, GeriMedica Inzicht landed 23 new customers, 7 trials, and 3 demos.
But Hamza took his data ideas even further and created The GeriMedica Dashboard Marketplace. This marketplace is a rich environment powered by Sisense dashboards and filled with documentation and training materials for end users to get their business insights. Customers don’t have to start from scratch. They can come to the marketplace and find solutions and peers across industries with the same goal: the best-quality patient care fueled by data-driven insights.
PRG digital transformation: Streamlining data with a hybrid approach in the cloud
Production Resource Group (PRG) is a leader in entertainment and event production, specializing in wide-ranging solutions that help bring the world’s greatest creative visions to life.
PRG has a vision of being a one-stop shop for its customers. However, over the past 30-plus years, there have been multiple acquisitions. With each acquisition comes the system integrations, one of the most challenging and critical success factors.
Tomas Lebovic, PRG’s Manager of Global Analytics and Reporting, wanted to streamline PRG’s data management systems and eliminate hurdles associated with combining data from acquired companies. He chose a combination of Sisense as an end-to-end BI solution and Snowflake as an elastic data warehouse. By combining the two platforms, PRG could scale its infrastructure, extending the impact to everyone, from C-level management down to front-line employees on the shop floor.
“Trying to get these multiple data sources merged together was a big issue for us. That’s where Sisense came in.”
— Tomas Lebovic, Manager of Global Analytics and Reporting at PRG
Lebovic said, “My team now relies on Sisense and Snowflake to simplify a variety of recurring data aggregation workflows, from profit and loss reports to sales analysis. Anything that used to require manually aggregating and merging spreadsheets can be pulled out of Sisense. Already, I’d say Sisense and Snowflake are saving our teams 100 hours per week.”
The Sisense end-to-end solution allows PRG’s data team to refocus its skills on delivering dynamic analytics applications to every level of the organization and automate the rest.
The business world is continuously changing, and the pace of those changes is accelerating faster than ever. You need to evolve to keep up. If your business isn’t already undergoing a digital transformation, you’d better be gearing up for one.
Digital transformation is not just about getting to the cloud and automating processes. It is not a product or solution to be purchased, and it affects everything IT touches in every industry. Leaders in today’s digital world need to be open to experimentation ― to trying something inherently new. The key is to convince people that what they were doing wasn’t wrong; it’s just that the world is different. The tools are changing. The opportunities are broader.
It is about changing both your technologies and business. Success today requires a new outlook, some bold steps, and insights to help make decisions for the future. And it can be done ― one dataset at a time.Watch demo