g-f(2)111 THE BIG PICTURE OF THE DIGITAL AGE, HBR, Why Is It So Hard to Become a Data-Driven Company?

Extra-condensed knowledge

• Thriving as a mainstream company today means being data driven.
• For the third consecutive year, investment in data and AI initiatives has been nearly universal, with 99.0% of firms reporting investment in data and AI according to findings from a newly released executive survey from NewVantage Partners, a strategic advisory firm that I founded in 2001 to advise Fortune 1000 companies on data leadership issues. But this year, despite growing investment, it appears most companies are struggling to maintain momentum.
• This year, a record 76.0% of respondents held the role of Chief Data Officer or Chief Analytics Officer. Survey respondents comprised the most senior corporate executives with oversight and responsibility for data within their firms.

Genioux knowledge fact condensed as an image

Condensed knowledge  

• Thriving as a mainstream company today means being data driven.
• For the third consecutive year, investment in data and AI initiatives has been nearly universal, with 99.0% of firms reporting investment in data and AI according to findings from a newly released executive survey from NewVantage Partners, a strategic advisory firm that I founded in 2001 to advise Fortune 1000 companies on data leadership issues. But this year, despite growing investment, it appears most companies are struggling to maintain momentum.
• This year, a record 76.0% of respondents held the role of Chief Data Officer or Chief Analytics Officer. Survey respondents comprised the most senior corporate executives with oversight and responsibility for data within their firms.
• Findings from this year’s survey suggest that even with record levels of committed investment, firms are continuing to struggle to derive value from their Big Data and AI investments and to become data-driven organizations.
• Often saddled with legacy data environments, business processes, skill sets, and traditional cultures that can be reluctant to change, mainstream companies appear to be confronting greater challenges as demands increase, data volumes grow, and companies seek to mature their data capabilities.
• Becoming a data-driven organization does not happen overnight. Building a data culture is a process. These efforts unfold over time. Today, Big Data and AI are mainstream, but there is still much work to be done.
• After nearly a decade of investment in data initiatives, are firms continuing to struggle in their efforts to become data-driven? 
• One answer is that becoming data-driven takes time, focus, commitment, and persistence. 
• Too many organizations minimize the effort or fail to correctly estimate the time which these kinds of wholesale business transformations require.
• Chief Data Officers and corporate data leaders should consider three pragmatic recommendations:
1. Organizations can benefit by focusing their data initiatives on clearly identified high-impact business problems or use cases. By starting where there is a critical business need, executives can demonstrate value quickly through “quick wins” that help a company realize value, build credibility for their investments in data, and use this credibility to identify additional high-impact use cases to build business momentum.
2. Companies must reexamine the ways that they think about data as a business asset of their organizations. Data flows like a river through any organization. It must be managed from capture and production through its consumption and utilization at many points along the way.
3. Data-driven business transformation is a long-term process that requires patience and fortitude. Organizations must show that they are in it for the long haul and stick with these investments and not lose patience or abandon efforts when results are not immediately forthcoming.

Category 2: The Big Picture of the Digital Age

[genioux fact produced, deduced or extracted from HBR]

Type of essential knowledge of this “genioux fact”: Essential Deduced and Extracted Knowledge (EDEK).

Type of validity of the "genioux fact". 

• Inherited from sources + Supported by the knowledge of one or more experts + Supported by a survey.

Authors of the genioux fact

Fernando Machuca

References

Why Is It So Hard to Become a Data-Driven Company?, Randy Bean, February 05, 2021, Harvard Business Review, HBR.

ABOUT THE AUTHORS

Randy Bean (@randybeannvp) is an industry thought leader, author, and CEO of NewVantage Partners, a strategic advisory and management consulting firm that he founded in 2001. He is a contributor to MIT Sloan Management Review, Forbes, Harvard Business Review, and The Wall Street Journal. Contact him at rbean@newvantage.com.

g-f(2)34 MIT SMR: Why Chief Data Officers (CDO) Must Assume Leadership for Data Success

Extra-condensed knowledge

• In their efforts to become data-driven, Fortune 1000 companies face the common challenge of identifying the right leadership that will enable them to overcome cultural and business roadblocks. 
• As companies struggle to manage data as a vital business asset, they must develop the leadership skills, expertise, and organizational structure to effectively manage and communicate the business value of data. 
• Companies must ensure that the chief data officer (CDO) has the necessary tools and support for executing on his or her data vision. 

Genioux knowledge fact condensed as an image.

The “genioux facts” Knowledge Big Picture (g-f KBP) chart

Condensed knowledge 

• Why Chief Data Officers (CDO) Must Assume Leadership for Data Success. As companies struggle to manage data as a vital business asset, they must develop the leadership skills, expertise, and organizational structure to effectively manage and communicate the business value of data. 
• Now more than ever, it is incumbent upon them to establish strong data leadership that will define and deliver on a data vision that supports the greater business vision of the company.
• In their efforts to become data-driven, Fortune 1000 companies face the common challenge of identifying the right leadership that will enable them to overcome cultural and business roadblocks.
• For most businesses, cultural issues manifest themselves in a variety of ways — resistance to change, antiquated business processes, a lack of clear coordination and communication of business imperatives and business value, ineffective organizational alignment, and uncertain leadership and commitment to data initiatives.
• These issues all point to serious gaps between ambition and execution that most organizations confront when embarking on data transformation efforts. 
• The Emergence of the Chief Data Officer (CDO). Today, the chief data officer role has emerged as a standard for most Fortune 1000 companies, but it comes with serious issues and challenges as companies struggle with how best to shape the role to achieve successful business outcomes.
• While a majority of CDOs — 54.6% — are now focused on revenue initiatives (offensive), a significant minority — 45.4% — remain focused on risk factors (defensive). Only 12.3% of CDOs have assumed direct revenue responsibility thus far, suggesting that moving into an offensive business-generation role will take some time.
• The Need for Business-Driven Data Leadership. Data challenges remain an issue for most companies — with only 37.8% reporting that they have established a data-driven organization, and only 26.8% saying that they have forged a data culture.
• The Data-Driven Business Imperative. Those companies that have learned to harness data, particularly as part of the digital experience, have been rewarded with rapid growth, customer expansion, and dominant market share and market value.
• Success is not assured. The long-term outlook for the role of the chief data officer remains uncertain. In 2019, 17.5% of survey respondents suggested that the CDO role was unnecessary.
• Companies must ensure that the chief data officer has the necessary tools and support for executing on his or her data vision. Only then will businesses be able to legitimately claim that they have earned the right to be called data-driven.

Category 2: The Big Picture of the Digital Era

[genioux fact extracted from MIT SMR]

Authors of the genioux fact

Fernando Machuca

References

Why Chief Data Officers Must Assume Leadership for Data Success, Randy Bean, November 30, 2020, MIT Sloan Management Review.

ABOUT THE AUTHORS

Randy Bean (@randybeannvp) is an industry thought leader, author, and CEO of NewVantage Partners, a strategic advisory and management consulting firm that he founded in 2001. He is a contributor to MIT Sloan Management Review, Forbes, Harvard Business Review, and The Wall Street Journal. Contact him at rbean@newvantage.com.

g-f(2)4 Companies need to identify the type of talent they need in order to become data-driven

Extra-condensed knowledge

Whether people are called data scientists, quantitative analysts, or something else, these titles are still relatively new to companies. It’s not surprising that there is little consensus on their meaning. Over time, new titles will emerge that better describe the particular activities involved in a job; data engineer, for example, has already arisen to describe people who spend most of their time wrangling data. Until more consensus is reached across our society, however, it’s important for companies to emulate TD Bank and clearly identify the types of talent required to become data-driven and then acquire it, nurture it, and unleash it.

Genioux knowledge fact condensed as an image.

Condensed knowledge 

• While many companies are hiring data scientists and other types of analytical and artificial intelligence talent, there is little consensus within and across companies about the qualifications for such roles. 
• The term data scientist might mean a job with a heavy emphasis on statistics, open-source coding, or working with executives to solve business problems with data and analysis. The idea of data scientist “unicorns” who possess all these skills at high levels was never very realistic.
• Colleges and universities have responded to the demand as well by offering hundreds of new programs on data science and analytics. But the skills taught in such programs vary widely, and some universities offer multiple programs with different emphases. For both newly hired and experienced employees, titles such as data scientist and quantitative analyst are not likely to be good guides to their actual capabilities.
• Developing new standards typically takes many years.
• Understand the Data Roles Needed. Analytics and AI talent is a scarce and valuable resource for every company, but particularly for those with a desire to be data-driven. That’s the situation at TD Bank Group, a large North American bank (the largest by assets in Canada). 
• TD executives made a series of internal investments directed at the critical importance of assessing, hiring, and motivating its data and analytics talent. Peter Husar, vice president of enterprise analytics strategy and planning, told me that he and his colleagues saw an opportunity to expand the role of analytics by first bringing consistency to the job definitions and positions related to data and analytics that existed across the bank.
• Husar and his team focused on understanding who was in their data and analytics community in order to provide a clear picture of their talent landscape.
• Acquire and Retain New Talent. In addition to more effectively managing existing data and analytics talent, organizations need to make a concerted effort to bring on and retain highly skilled employees.
• Build a Data and Analytics Community. TD data and analytics leaders are conscious of the need to build community and knowledge-sharing approaches among the data and analytics professionals across the bank. That includes providing opportunities for data- and analytics-oriented employees to network and hear about trends in the industry. Five years ago, the bank had its first annual Big Data & Analytics Summit, with 40 employees attending. In 2019, the bank had its fifth annual summit, with over 2,000 employees in attendance — and the numbers continue to grow.
• TD’s initial job-mapping exercise led to a clearer vision for how individuals can move around, upskill, and grow their career paths. The team spearheading the plan created educational materials to help data and analytics employees develop their skills and made those materials available on an enterprisewide online platform for self-learning and development.

Category 2: The Big Picture of the Digital Age

[genioux fact extracted from MIT SMR]

Authors of the genioux fact

Fernando Machuca

References

How Large Companies Can Grow Their Data and Analytics Talent, Thomas H. Davenport, November 18, 2020, MIT SMR, MIT Sloan Management Review. 

ABOUT THE AUTHORS

Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, as well as a fellow at the MIT Initiative on the Digital Economy and senior adviser to Deloitte’s Analytics and AI practice.

Extracted from Wikipedia

Thomas Hayes "Tom" Davenport, Jr. (born October 17, 1954) is an American academic and author specializing in analytics, business process innovation, knowledge management, and artificial intelligence. He is currently the President’s Distinguished Professor in Information Technology and Management at Babson College, a Fellow of the MIT Initiative on the Digital Economy, Co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.

Davenport has written, coauthored, or edited twenty books, including the first books on analytical competition, business process reengineering and achieving value from enterprise systems, and the best seller, Working Knowledge (with Larry Prusak) (Davenport & Prusak 2000), on knowledge management. He has written more than one hundred articles for such publications as Harvard Business Review, MIT Sloan Management Review, California Management Review, the Financial Times, and many other publications. Davenport has also been a columnist for The Wall Street Journal, CIO, InformationWeek, and Forbes magazines.

g-f(2)3 Trends of a Google search on Digital Transformation, 11/18/2020

VIRAL KNOWLEDGE: The “genioux facts” knowledge news

Extra-condensed knowledge

A Google search on digital transformation gave me only 2 articles published on November 18, 2020. 

• Top 8 trends shaping digital transformation in 2021, Vala Afshar, November 17, 2020, ZDNET. 
• Avoiding Four Common Digital Transformation Obstacles, Maxim Ivanov, Nov 18th, 2020, Manufacturing Net. 

Genioux knowledge fact condensed as an image.

Condensed knowledge 

1. ZDNET, Top 8 trends shaping digital transformation in 2021. Research from Salesforce MuleSoft and third-party findings highlight some of the top 2021 trends facing CIOs, IT leaders, and organizations in their digital transformation journey. 
   • According to Salesforce MuleSoft Connectivity benchmark report (57 pages), IT projects are projected to grow by 40%; and 82% of businesses are now holding their IT teams accountable for delivering connected customer experiences. 
   • Research from MuleSoft proprietary research and third-party findings highlight some of the top trends facing CIOs, IT leaders, and organizations in their digital transformation journey. 
   • Here are the top 8 trends shaping digital transformation in 2021. 
   1. The digital-ready culture. 
   2. Democratization of innovation.  
   3. Composable enterprise. 
   4. Automation. 
   5. API security. The average enterprise has 900 applications. The proliferation of new endpoints creates new avenues for intrusion, requiring robust API security. 
   6. Microservices. Organizations are turning to microservices to rapidly build new customer experiences. Companies deploying microservices to production will require some form of service mesh capabilities to scale. 
   7. The data divide. To keep pace with evolving customer expectations, organizations are looking for faster ways to unlock data and gain insights.  
   8. Data analytics. Organizations are investing in data analytics to transform customer experiences. The value of data analytics will be dependent on the data they are fed.
2. Manufacturing Net, Avoiding Four Common Digital Transformation Obstacles. In the era of growing demands and rapid changes, companies are turning to digital transformation more than ever before to make their businesses more competitive, improve sluggish activities, and increase revenue.
• Before examining possible pitfalls and the ways to deal with them, let’s first dive deeper into the three primary advantages of this trend.
1. According to The Economic Times, employees spend almost 25 percent of their working time searching for the required information, and about 50 percent of their time performing routine tasks. 
2. Another benefit, in terms of finances, is related to the reduction of operational costs. The absence of highly-functional digital tools has a direct correlation to excessive spendings. 
3. Companies can obtain a better comprehension of their customers’ needs and requirements.
• According to the research conducted by Gartner, 56 percent of CEOs confirmed that digital transformation resulted in a significant revenue increase.
• Digital transformation is a complicated process that may not bring the desired results if it isn't approached correctly. According to the Forbes survey, almost 50 percent of the companies fail due to the inability to conduct this transformation properly. Below you will find the four most common challenges the companies face on their way to digital changes.
1. Absence of a thoroughly-designed strategy. 
2. Employee unwillingness to adapt to changes.
3. Lack of proper expertise and required technical skills.
4. Budget management.

Category 2: The Big Picture of the Digital Age

[genioux fact extracted from ZDNET, Manufacturing Net]

Authors of the genioux fact

Fernando Machuca

References

1. Top 8 trends shaping digital transformation in 2021, Vala Afshar, November 17, 2020, ZDNET. 
2. Avoiding Four Common Digital Transformation Obstacles, Maxim Ivanov, Nov 18th, 2020, Manufacturing Net.