Digital Transformation Begins with Digital Analytics

Stephen DeAngelis

June 3, 2021

Regardless of the economic sector in which your organization resides, to survive and thrive, experts insist it must adapt to the Digital Age. Contemplating digital transformation can be daunting and some executives are not sure where or how to begin. Many experts suggest the digital transformation journey must begin with data and its analysis. “In today’s world,” explains Vishal Jain, Director at Inspira Enterprise, “data is considered the most valuable asset and can be a differentiating factor for the success of an organization. However, this can only be achieved when organizations can effectively use data generated by them. And here is where Data Analytics can be a game-changer. Analytics is changing the way businesses are done, giving way for novel business models and revenue streams.”[1] Identifying the right data for your organization can be difficult since some data may reside in some other company’s database. A false step in assembling the right data can sidetrack your transformation efforts. Once you have identified, and have access to, the right data, you must do something with it. Jain explains, “The insights derived from data are not only useful to drive digitization and automation of workflows but also play a key role in the decision-making process. It underpins most digital transformation efforts.” Tech writer Bob Violino (@BobViolino) adds, “Finding success with data analytics requires good tools, good data management, and good strategy.”[2]

 

Beginning Your Data Analytics Efforts

 

“There’s no question that data analytics can be a significant competitive differentiator for companies,” Violino writes. Why are analytics so important? Tech writer Shelby Hiter (@ShelbyHiter) explains, “Everything measurable that has happened, is happening, and will happen in a business can be boiled down to data. But not all data is immediately comprehensible to the human mind, so data analysis steps in to elucidate meaning. Data analysis is the practice of cleaning, modifying, and examining data to find helpful information. The function enables business analysts to understand how variables connect and derive actionable insights from data sets.”[3] According to Violino, “[Good analytics can deliver] insights that can help boost sales and market share. But how much enterprises gain through analytics can depend a great deal on how well they’re taking advantage of the latest technologies, and how prepared they are for future developments.” Before you start looking at technology, IT consultant Bob Lewis (@ITCatalysts) suggests you look at your corporate culture. “For far too many organizations,” he asserts, “analysis has been corrupted by a culture of tweaking parameters to support desired outcomes.”[4]

 

If you don’t think your company can fall victim to a corrupted culture, think again. Cortney S. Warren (@DrCortneyWarren), a board-certified clinical psychologist and Adjunct Clinical Professor of Psychiatry at the University of Nevada School of Medicine, insists, “Humans are masters of self-deception. We fool ourselves into believing things that are false and we refuse to believe things that are true. In fact, we lie to ourselves about just about everything. … And most of the time, we are completely unaware of the rampant lying going on in our own minds.”[5] Lewis rightfully observes that data analysis should take place in a culture of honest inquiry. Unfortunately, he notes, “[Managers have] learned to adjust their assumptions — poking, prodding, and tweaking parameters — until their [data analysis] infallibly arrived at the answer they wanted. The result? Cultures across corporate America in which data lakes, data marts, data warehouses, and analytics software have become little more than platforms for managerial parameter tweaking. They’re used, not for illumination, but as ammunition by those who start with the decision they want and work backward to find the filters and parameters needed to support it. And this is the dirty secret that is sinking your analytics strategy.”

 

Let’s assume, however, that your organization has the right data and the right culture. What is it that you are looking to get from your data? Hiter notes there are a number of ways data can be analyzed. They include:

 

  • Text analysis: “One of the most common types of qualitative analysis, text analysis involves translating human text, so it can be processed through machine learning for more valuable insights. Text analysis, or text mining, is a text-based form of data mining.”
  • Descriptive or statistical analysis: “The earliest step in data analysis, descriptive analysis involves organizing and manipulating raw data to be understood and interpreted in further analysis. It is usually numerical and statistical in nature.”
  • Inferential analysis: “This form of analysis focuses on a sample group within the whole, making hypotheses based on generalized trends across a data set. It is often used in market research and statistical analysis.”
  • Diagnostic analysis: “If you’re faced with a problem tied to numbers or outcomes, diagnostic analysis helps you to find the ‘why’ behind events and relationships in your data set. This is considered one of the most important forms of analysis in business settings.”
  • Exploratory analysis: “Often used in combination with text analysis or data mining, exploratory analysis involves examining the relationships between the data and its variables. This is a key category of analysis for problem solving.”
  • Predictive analysis: “Relying on a mix of the other analysis categories as well as machine learning (ML) and artificial intelligence (AI), predictive analytics uses existing data to forecast data outcomes.”
  • Prescriptive analysis: “As the most complex form of data analysis, prescriptive analysis combines all of your data and analysis to determine potential outcomes and actions to take based on your findings. AI is a growing subfield within prescriptive analysis.”

 

Leveraging Technology

 

With the right data, a good strategy, and a full understanding of what you desire from your data analysis efforts, finding the right technology to achieve your goals is essential. Tech writer George Lawton (@glawton) suggests a dozen “must-have big data analytics features that can help reduce the effort required by data scientists and other users to produce the desired results.”[6] Those features are:

 

1. Embeddable results for real-time analytics and reporting. “Big data analytics initiatives gain more value for organizations when the insights gleaned from analytics models can help support business decisions being made on the fly, often while business executives and managers are using other applications.”

 

2. Data wrangling and preparation. “Data scientists tend to spend a good deal of their time cleaning, labeling and organizing data to prepare it for analytics uses. The data wrangling and preparation process involves seamless integration across disparate data sources, plus steps that include data collection, profiling, cleansing, transformation and validation.”

 

3. Data exploration. “Sometimes considered to be part of data preparation, exploring the underlying data helps data science teams understand the business context of a problem and formulate better analytics questions.”

 

4. Support for different types of analytics. “There are a wide variety of approaches for putting big data analytics techniques into production use, from basic BI applications to predictive analytics, real-time analytics, machine learning and other forms of advanced analytics. Each approach provides different kinds of business value. Good big data analytics tools should be functional and flexible enough to support these different use cases with minimal effort and without the retraining that often might be required when adopting separate tools.”

 

5. Scalability. “Big data analytics systems [must] support high levels of scalability for ingesting data and working with large data sets in production without exorbitant hardware or cloud services costs.”

 

6. Version control. “Version control features built into big data analytics tools can improve the ability to track changes. If problems do emerge, [version control] can make it easier to roll back an analytics model to a previous version that worked better.”

 

7. Simple data integration. “Simple data integration and access capabilities make it easier to share analytics results with other users. Big data analytics tools should provide built-in connectors and development toolkits for easy integration with existing databases, data warehouses, data lakes and applications — both on premises and in the cloud.”

 

8. Data management. “Strong data management features can help an enterprise maintain a single source of truth, which is critical for successful big data initiatives.”

 

9. Data governance. “Effective data governance is especially crucial for sensitive data, such as protected health information and personally identifiable information that is subject to privacy regulations.”

 

10. Support for data processing frameworks. “Big data analytics tools need to have ties to various processing engines that can help organizations build data pipelines to support the development, training and implementation of analytics models. This is important because data science is a highly iterative process.”

 

11. Data security. “Excessive data security can discourage engagement with analytics data. But big data analytics tools that include well-designed security features can address IT concerns about data breaches while also encouraging appropriate data usage.”

 

12. Data visualization, dashboard design and reporting. “Ultimately, data scientists and analysts need to communicate the results of big data analytics applications to business executives and workers. To do so, they need integrated tools for creating data visualizations, dashboards and reports, along with capabilities for managing the data visualization and dashboard design process.”

 

Companies that master data analytics are well on their way to becoming digital enterprises and will reap the rewards of their efforts. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer) explain, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”[7]

 

Footnotes
[1] Vishal Jain, “Why Big Data Analytics is the first step towards digital transformation,” Express Computer, 19 February 2021.
[2] Bob Violino, “How to excel with data analytics,” InfoWorld, 12 April 2021.
[3] Shelby Hiter, “Best Data Analysis Methods 2021,” Datamation, 3 May 2021.
[4] Bob Lewis, “The dirty secret of data analytics: Culture of honest inquiry required,” CIO United States, 7 May 2021.
[5] Cortney S. Warren, “How Do I Know When I Am Lying to Myself?” Psychology Today, 28 May 2014.
[6] George Lawton, “12 must-have features for big data analytics tools,” TechTarget, 28 April 2021.
[7] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.