Executive Bytes

09.19.2022 Executive Data Bytes – CDO Leadership: Present & Future

September 19, 2022

The prevalence of CDO roles has grown globally in many sectors. To be effective, a CDO must be focused on business value, balance enablement and control, and view data and analytics as a value chain instead of a centralized, isolated silo. Read on in this CEOWorld Magazine article to learn more about the future of the CDO role.

Global data creation is expected to reach 175 zettabytes by 2025, and the global big data and analytics market is on pace to reach $135.71 billion. Companies worldwide are scrambling to reengineer their data strategy. The CDO role is one of the newest potential additions to an organization’s senior leadership team, and comprises seven critical job types. The CDO role may differ from one business to another, depending on the market context. To remain competitive, organizations will need to transform their data systems. CDOs are crucial to this process

“Data is the lifeblood of an organization, but many businesses aren’t investing enough in deriving true value from it. This is why the Chief Data Officer (CDO) role has become so popular in recent times. This article from Intelligent CXO lists five reasons why CDOs are the leaders of the future. A CDO is a trained data strategist that can interpret data and make the best use of it. He or she helps to make data less scary and more open and ensures that every employee understands the value of data-driven strategies.

The initial incarnation of the chief data officer (CDO) was more of a governance job than an analytics-focused one. This Forbes article explains how the CDO role is starting to split into several different functions. In the beginning, there was a lot of talk about being data-driven, but as these data stores expanded and evolved, companies recognized that they were not just assets but liabilities, and started to organize and make data actionable. With the shift from focusing on governance to focusing on analytics, it’s important to rethink data architecture.

09.12.2022 Executive Data Bytes – Innovate with cloud and reach business goals NOW

September 12, 2022

For businesses, the race towards accelerating innovation is (or should be) the highest priority. In this article, TechCrunch outlines the benefits and challenges of adopting cloud. Public cloud technology allows businesses to quickly test out new technologies, scale up or down as needed, and retain data in a specific geographical region for privacy and regulatory compliance.

Recently, unexpected events have exposed significant structural weaknesses, which are amplified by global trade disputes, environmental disasters, and civil unrest. This EY article explains how cloud technology can enable organizations to transform quickly and reframe their future. Businesses should prioritize speed and resilience over cost optimization and monitor data in real-time to pre-empt adverse outcomes and gain significant competitive advantages.

The future of cloud appears to demand a new approach that considers business, technical, and financial priorities together to gain greater value from cloud innovation strategies. Read on to explore four potential scenarios for the future of cloud innovation strategy in this article by Deloitte. Cloud technology can help organizations innovate across today’s enabling, tomorrow’s disruptive, and future next-horizon technologies. The CIO should guide organizations in their use of cloud technology to innovate and help them understand the business, technical, and risk sides of the innovation equation.

08.29.2022 Executive Data Bytes – Embracing Data Literacy and Its Culture

August 29, 2022

A lack of professional development is one of the top reasons for employee turnover. Businesses cannot afford to wait and allow other, more data-forward enterprises to win top talent. This Forbes article explains why data literacy education is crucial for success. The modern workplace is rapidly changing, and data plays a central role in many of those changes. However, despite these changes, only 21% of employees believe their employer is preparing them for a more data-oriented and automated workplace.

Today, companies are amassing more data than ever before, and also undergoing a digital transformation to become more data-driven. Given the changes in the data and analytics landscape, non-data professionals need to become data literate. Read on in this article from Eliiza to learn more about how both business and employees can benefit from improving data literacy across the workforce. Businesses are adopting a data mesh architecture where data and analytics functions are delegated to individual business domains.

A data-driven culture is an organizational mindset that uses data to inform business decisions, predict customer behavior and drive transformative technologies. To create a data-driven culture, follow these 10 steps in this article from Hexacta. The key to making complex data work for a solution-oriented management system is asking the correct questions and knowing how to solve problems. Root Cause Analysis can help identify business problems and priorities early on.

08.22.2022 Executive Data Bytes – Deriving business insights and making sound decisions based on findings

August 22, 2022

Data is valuable only if it can be translated into insights, and organizations are still struggling to see insights clearly through their data. In this article, Treehouse Technology Group outlines the steps you can take to derive actionable insights from your data. To turn data into useful information, you need to put context to the data and establish why the data is important. You also need to anticipate the outcome of the project and elaborate on how the results will be used or integrated into the business.

Top-performing companies use data to make decisions about their business, and these companies gain a strategic advantage over their rivals. This blog from Sisense Team aims to help you improve your decision-making process. Data-driven companies are more customer-focused, enjoy deeper insights into the customer and their journey, and are more agile and better able to respond to markets.

Due to the recent advancements in modern technology, data-driven decision making is starting to be employed across various industries. This article from Softjourn explores the ways various companies are benefiting from data-driven decisions. Data-driven decision making (DDDM) uses past information to predict what will happen in the future. KPIs are used to measure the performance of systems and to see how close they are to achieving business goals.

08.15.2022 Executive Data Bytes – Data Fabric: What it is, Why it is, And Do You Need It

August 15, 2022

A data fabric is an architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning hybrid multi-cloud environments. It enables a unified data management architecture so that enterprises can gain from an extensible and converged data capability. This article from Spiceworks outlines what you really need to know about data fabric. While we live in a data-driven age, organizations spend a disproportionate amount of time on routine tasks and not enough on value addition. Data fabric rectifies this imbalance by removing back-end bottlenecks in data management.

Data management is prone to human errors. If data and analytics leaders want to reduce these errors, and overall costs, it’s crucial that they shift toward modern solutions like AI-enabled data integration. In this article, Gartner says the emerging design concept called ‘data fabric’ can be a robust solution to data management challenges. Data fabric works like a car’s autonomous element. It monitors the data pipelines as a passive observer at first, and then starts suggesting alternatives that are far more productive.

Data fabric architecture has emerged as a way to facilitate data exchange among the many disparate systems that support the business. However, like anything, data fabric architecture has pros and cons. This ITPro Today article explores some of these advantages and disadvantages. A data fabric is a technology infrastructure used to access and move data, and is often positioned as a disruptive, zero-sum architecture. However, data fabric is a complement to, not a replacement for, data management tools, practices and concepts.

08.08.2022 Executive Data Bytes – ‘Who’s ON First?’​ – Prioritizing your AI projects for success

August 8, 2022

Cloud storage and computation have enabled the efficient processing of huge data sets to draw critical insights using Artificial Intelligence (AI). However, with so many options, deciding where to devote your resources is the most difficult part. Learn more about how you can prioritize your AI projects in this article from The Enterprisers Project. Prioritization helps businesses achieve long-term success by taking all factors into consideration and making thoughtful decisions, rather than executing AI projects on an ad hoc basis.

Many businesses are considering how Artificial Intelligence (AI) and machine learning (ML) fit into their strategy. This article from Excella explores why your organization must decide on a set of needs and goals, and why it’s imperative to decide what level of accuracy is required and how timely insights need to be. AI/ML projects are challenging because they contain significant unknowns, require rapid exploration and fast feedback to learn and adjust, and involve uncertainty with the data, modeling algorithms, and the interactions of key variables.

Machine learning is a truly momentous time for the enterprise, with investments soaring and a growing number of use cases that can create tangible business value. However, many organizations are still struggling with important phases of the AI/ML lifecycle, including governance. In this article, TechBeacon explains the importance of Ai/ML governance. AI/ML governance is the overall process for how an organization controls access, implements policy, and tracks activity for machine learning models. It includes regulatory compliance and audit risk, but also forms the bedrock for minimizing risk while maximizing ROI.