In the days of analog operations, processing information and using it to further your company objectives were typically limited to targeted — and often costly — initiatives that involved field experts, statisticians, and data analysts. Processing large swaths of data were beyond the capabilities of most contemporary technologies and were often cost-prohibitive for smaller organizations. Digital transformation was the technology that only the upper echelons of the industry could afford. Prioritizing data analytics and digital transformation was, for the most part, a recommendation, something that was highly encouraged but not always seen as necessary for the sustained growth of the organization.

With the advent of technological innovations over the last two decades, there has been a paradigm shift in how data is processed, analyzed, and leveraged at a reasonable cost for all organizations in the economic spectrum. Today, all businesses wanting to achieve their long-term goals around growth, social responsibility, and competitive advantage are investing in digital technology — including “Internet Of Things” (IoT), “Process Filtering,” “Artificial Intelligence” (AI), “Machine Learning” (ML), and “Data Mining” — that can achieve their objectives with a few simple and smart initiatives. Now, digital transformation is not just recommended, it’s expected — especially given the pace of technological innovation and its rate of adoption, which only accelerated when the COVID-19 crisis exposed large gaps in demand speculation and the supply chain. While the setback to the overall global economy wasn’t as harsh as some of the earlier studies suggested, the pandemic nevertheless revealed myriad vulnerabilities within current business intelligence operations.

Generally speaking, those organizations that digitized their work processes and leveraged digital technology before the pandemic were able to overcome the disruptions that resulted in their supply chains and other areas of production. In fact, “digitally mature organizations” are more likely to have increased their profit margins and annual revenue growth during the pandemic compared to businesses whose processes weren’t as digitally mature. These same companies said digital transformation was central to their financial success by helping to improve the customer experience.

It’s little surprise, then, that digital transformation continues to be an important investment for businesses. As Gartner reported, in the United States alone, information technology spending is on pace to reach $4.4 trillion by the conclusion of 2022. Much of that spending is in software, such as infrastructure as a service (IaaS). And last year, nearly three-quarters of organizations in a separate Gartner poll said they were actively engaged in digital transformation initiatives (24% leading, 48% heavily involved).

To what degree are businesses investing in digital transformation?

Digital transformation means different things to different organizations, depending on their needs, goals, specialties, and the products or services they provide. For example, among banks and credit unions, digital transformation may involve rolling out mobile apps that customers can use for remote banking capabilities. For other types of businesses, digital transformation may involve leveraging digital assistants, work-from-home solutions for staff, or chatbots on their websites to help with customer concerns or questions. During COVID-19, call centers experienced substantial growth in call volume because of the lockdown measures. As Forbes reported, organizations with technology in place that allowed employees to field calls from their homes had an easier time navigating the pandemic.

But regardless of how digital transformation manifests itself, organizations have stepped up their efforts to make it happen. According to the International Data Corporation, the combined amount of money that businesses worldwide are expected to spend on digital transformation activities is poised to reach $6.8 trillion by 2023, an increase of 15.5% from 2020. By next year, fully 75% of enterprises are expected to have a “digital transformation roadmap” in place, outlining the steps involved.

But much like a standing structure needs a foundation, every successful digital transformation journey requires a starting point. And for digital transformation to be successful, your business must first begin with data and analytics. This article will help you understand why data analytics is central to digital transformation. As you’ll see, just because organizations take steps toward digital transformation doesn’t mean they’ll always be successful in those efforts.
 

 

Data analytics explained

Data and analytics go hand in hand. In fact, the terms are used so frequently together, that it spawned the hybrid term “data analytics.” Data analytics refers to the ongoing measurement and gathering of statistics, facts, observations, and other pieces of information that can be used to make smart, well-thought-out business decisions. Grouped into four forms  — predictive, prescriptive, diagnostic, and descriptive — data analytics help to identify trends, answer questions, map out solutions, explain why events happened, and draw conclusions.

Bottom line: Data analytics not only helps you make decisions with respect to your business but make the right ones since they’re grounded in actionable intelligence.

1. Predictive
   As its description implies, predictive data analytics leverages quantitative techniques to forecast potential outcomes. From machine learning to data mining to statistical modeling, such methodologies can help to identify trends, cause and effect, and patterns in behavior in terms of end results.
2. Prescriptive
   Prescriptive analytics builds off of predictive analytics; they are often used in combination. While predictive helps to identify trends, prescriptive data analytics outlines the proper course of action in light of those forecasted outcomes. Whether it’s through artificial intelligence or big data, prescriptive analytics provide possible solutions by testing the prescribed actions.
3. Diagnostic
   Whereas prescriptive and predictive analytics are more forward-looking — answering the “what?” about something — diagnostic data analytics is more historical in nature, answering the “why?” about things turning out the way that they did. This part of data analytics is where the actual analysis takes place to explain relationships from a standpoint of cause and effect. Regression analysis, data discovery, and data mining are some examples of diagnostic data analytics techniques.
4. Descriptive
   Descriptive analytics is the data type that helps to draw conclusions. Whether it’s combing through historical data, comparing and contrasting numbers from one year to another (e.g., sales, prices, subscribers, etc.), or assessing other key performance indicators, descriptive analytics answer the most questions, including what, where, when, and how many. Descriptive analytics also assists with reporting, a core function for businesses regardless of their size or industry.

Data analytics is the key to digital transformation success

Forbes has reported that just a few years ago, approximately 85% of companies that took the initial steps toward digital transformation were unsuccessful in their attempts. Michael Gale, an industry expert in integrated technology, told Forbes that part of the reason for the high failure rate has to do with the inability to help employees manage the shift in processes. In short, organizations often struggle with change management and preparing workers for some bumps along the road. “Basic awareness about those challenges is probably the key indicator of how well the process will be successful,” Gale told Forbes.

The main complicating factor, however, is not fully appreciating the indispensability of data in digital adoption. Data is essential to guiding informed business decisions. In 2019, fewer than half of corporate strategies that Gartner reviewed referenced data analytics as being fundamental to delivering enterprise value. Douglas Laney, an analyst at Gartner, said that for businesses to be successful in the increasingly digitized economy, data and analytics are must-haves. “A company’s ability to compete in the emerging digital economy will require faster-paced, forward-looking decisions,” Laney explained. Laney added that corporate heads must ensure that their staff members have at least a basic, high-level understanding of data, analytics, and data science. But this is a challenge for many companies. According to a poll from the Harvard Business Review, just 25% of surveyed workers were confident in their ability to understand and interpret data.

Establish a data-driven culture and improve data literacy

How does an organization improve data literacy? One way is by establishing a data-driven culture. This involves not only bottom-lining data so that the average layperson can understand but also helping workers see how data can help solve business problems at all levels of an organization, from the supply chain to inventory management to worker productivity.

Another way is by simply discussing data and analytics more often so it’s not a foreign, abstract concept. This may even include creating positions within the company that specialize in all things data and data management, such as a chief data officer (CDO). “Increasing data literacy inside the organization enables D&A leaders and CDOs to implement a data-driven culture which encourages the use of data in decision-making,” said Debra Logan, vice president of research at Gartner. Logan added that data analytics is “fundamental to digital business transformation and can deliver value if the CDO addresses both data and business priorities.”

Another way to both build data literacy and establish a more well-entrenched data-driven culture is by asking a series of “How many?” questions. For example, how many workers would be able to interpret traditional statistical operations, such as correlations? On the management side, how many team leads would be able to construct a business case that is numbers-based? How many managers would be able to elaborate on the output they’re getting from their systems? Would those same managers be able to explain the output of their machine learning algorithms, or would that only be something a data scientist could address? The answers to these questions can help determine the strategy needed to build a more data-literate organization.

Craft an effective digital transformation strategy with data analytics as the foundation

With the correct data analytics and actionable intelligence in place, organizations can craft a digital transformation strategy with a strong foundation. Just as digital transformation can mean different things to different people, the same can be said for transformation strategy. McKinsey & Company offers a few suggestions regarding what every transformation strategy ought to include. A prime component to success is crafting a transformation team:

• Clearly define the mission: It isn’t enough to roll out digital tools “just because” or to approach the transformation with an “everyone else is doing it” mentality. There needs to be a reason for it, one that is holistic in scope, measurable, and understood by all.
• Ensure there’s collaboration: Digital transformation can’t be in just one portion of the company. It needs to be implemented with an all-hands-on-deck style approach, involving all channels and departments. When building a digital transformation team, the membership should be similarly diversified.
• Delegate authority: While it’s important for digital transformation teams to work together, there is also something to be said for allowing members to “own” their responsibilities. Corporate leaders need to define the goal and/or mission, then get out of the way and let the team members do their thing.
• Be judicious: While a good balance of team membership is important, the composition of the team requires serious thought. For example, you shouldn’t just appoint individuals to the team because the workers you wanted weren’t available.
• Build a central team: Ideally, digital transformation teams should be complemented by a centralized team that is there to support those who are leading the effort. They can assist with coordination, allocating resources, maintaining best practices, and reviewing progress.

From developing a strategy to proofs of concept to implementation, Inspirage can help you during every stage of your digital transformation journey. Contact us today to learn more.

If you think you are getting the best value from Tableau you might want to think again. When you weigh the pros and cons you could discover that a more robust, enterprise-wide autonomous analytics tool is not only a better value, but saves valuable time with IT resources, up-time and ease of development.

Tableau vs. Oracle Analytics Cloud:
Before we begin let’s take a few moments to compare Autonomous to Managed solutions. If you are an organization that has limited IT resources Oracle’s Autonomous Analytics Cloud might just be the thing that seals the deal. Consider a system that manages itself. Very little intervention is required once you provision your services. The system patches, performs maintenance, does backups and most of those other tedious tasks required to maintain an enterprise software solution. What you can save on IT resources and expertise might be just the choice you are looking for in your Analytics and Data Visualization package.

Two Major Selling Points:
Two major selling points for Oracle Analytics Cloud for many organizations are the inclusion of Essbase Cloud in the product suite and OAC as a complete enterprise solution with an enterprise platform and security. If you are looking for a big data solution, OAC has that as well. With the addition of BIC– Business Intelligence Cloud you can have a more traditional BI solution as well as easily managed dashboards to display all your reporting needs.

Ease of Use:
For ease of use either tool is an excellent choice. The drag and drop interface on either tool is simple, clean and direct which assists users in exploring their data in foundational ways. Vibrant visualizations can be built allowing either tool to choose the format that best expresses the data or customize for a personal solution. Visualizations can be overlapped, interlaced or tiled in either tool. And both have a wide array of report and chart types that fit nearly any need.

Is High Performance Your Benchmark?
If high performance is your benchmark either solution will work for you. Both products can return speedy queries for a full spectrum of data visualizations. Additionally, both products produce high quality, brilliantly stunning data visualizations. Tableau may have a slight edge on flexibility with some of the advanced functionality for visualizations being more adaptable—the color pallet being one. Tableau allows the user to modify and slide the color pallet to customize the weight of each color. OAC is more automatic and does not allow the user to slant the results by adjusting the color pallet to highlight specifically chosen information. Apples and oranges, if you are more interested in providing a non-biased picture of your report, OAC is your tool. Both tools allow interaction between visualizations— click one viz and the other responds with the corresponding information. This functionality is as simple as check a box in OAC and only slightly more complicated in Tableau.

Data Integration:
Let’s move on to data integration. Both products have an impressive choice of data connectors that are continually being enhanced but that is where the similarity ends. Tableau requires a moderate to advanced understanding of SQL to build data relationships and queries. OAC has a feature called Data Flows that through a drag and drop interface allow you to perform Extract Transform and Load features like an expert. Data Flows use a combination of out of the box features that can intuitively build your Data Flow to create defined, curated data sets for your visualizations. And once those Data Flows have been built you can create a sequence to schedule and manage them. Additionally, you’ll find some nifty configurable Machine Learning drag and drops that let you train and explore your data like a pro.

Do You Need a Forum for Your Data?
If it’s a forum that you need for your data, Tableau is unmatched. There are many ways to share your visualizations including Tableau’s robust public solution. You’ll find dozens of blogs, webpages and other venues extending and enriching the Tableau footprint. The Tableau user community is wide-ranging and dense with potential solutions for any visualization you might choose to develop. OAC, a new product more focused toward the Enterprise, has a footprint that is just beginning to expand. The Analytics Cloud community has developed an assortment of downloadable plugins that offer a wide range of functionality including one that solves that pesky need for hierarchical reporting with a crazy simple click and drag.

Comprehensive Mobile Solution:
Both tools have a comprehensive mobile solution that can be accessed on phone, tablet or laptop in either Android or iOS. Both products have interactive interfaces with Tableau taking the lead for comprehensive usability. The newer OAC mobile app, Day by Day, allows less phone functionality, but the question is how much reporting interactivity do users want on something as small as phone.

OAC – Your Go To for a Holistic Solution:
For a holistic solution including collaboration, analytics, big data and security, OAC is the hands down champ. Because it is an enterprise solution offering the opportunity for across the board reporting it includes features in which Oracle excels. If you need to create dashboards for users with selective visibility, OAC with BICS is your tool. If you choose to use one of Oracles new Autonomous databases for your data not only can you integrate them to any portion of the product, but they provide Enterprise-wide security and collaboration. OAC is a one-stop shop encompassing all your reporting needs.

Ultimately, the choice comes down to how, what and when you do your reporting. If a visualization is all you need, Tableau could be the perfect solution. But if your organization needs something more comprehensive that pulls all your reporting into a single cloud-hosted environment, OAC might be just what you are looking for.

Catherine Summers
BI Practice Lead
Vertical Edge Consulting Group
csummers@verticaledgecg.com