What is a data strategy?

What is a data and analytics strategy for AI?

Data powers not only AI but the analytics required for accurate decision-making. A deliberate data strategy ensures your organization has the information it needs to seize the opportunity offered by rapidly advancing AI technologies. An effective data and analytics strategy involves:

• Defining your data practice vision with a step-by-step strategy on value streams, mapping capabilities, data practice maturity, and your operating model.
• Assessing your skills gap and developing your approach to partnering and communicating with stakeholders.
• Defining a framework for your data products.
• Creating a data roadmap, with clear priorities and action items.

Define a Data Practice Strategy to Power an Autonomous Enterprise

Exponential IT transformations cannot be achieved without data, analytics, and AI capabilities.

Analyst Perspective

Technology and data leaders need to deploy new tactics to meet the demands for data that the AI revolution commands.

Applied AI is a true paradigm shift offering opportunities for transformative value generation and potential for disruption. AI will continue to demand an increasing volume and variety of quality data, delivered increasingly in real-time, produced and consumed from anywhere.

This exponentially growing demand for data increases the pressure on technology and data leaders to deliver the processes, capabilities, and technologies to service that demand; however, with only one in ten data leaders considered genuine business value generators by business executives, leaders will be required to develop new tactics to meet these expectations or risk being left behind.

Data & analytics practices need to change. CIOs need to lead the change by partnering with business and empowering business leaders to own and drive data & analytics initiatives. The practice crosses over technology and business, practice capabilities are integrated into the IT and business capabilities. Organizations need to redefine the data & analytics practice and its capabilities, break down IT and business silos, and recognize they have skills gap – data teams need to learn how to communicate with the business in terms of business value, and business teams need to learn data product management.

Solving for exponential data growth will emphasize the need for change. The future for data leaders is bright for those bold enough to embrace the change, not be carried away by it.

Irina Sedenko
Research Director
Info-Tech Research Group

Vince Mirabelli
Principal Research Director
Info-Tech Research Group

Executive Summary

Your Challenge

• AI will continue to demand an increasing volume and variety of quality data, delivered in real-time, produced and consumed from anywhere. As a result, the complexity of problems solved with data and AI will increase exponentially.
• This exponentially growing demand for data increases the pressure on technology and data leaders to deliver the processes, capabilities, and technologies to service that demand.
• Leaders will be required to develop new tactics to meet these expectations or risk being left behind.

Common Obstacles

• Wasting time and resources on managing all data assets equally, missing business opportunities due to a lack of understanding of the most valuable data assets as the actual differentiator in value generation.
• Inability of Data & Analytics team to meet business needs to innovate, grow, create differentiators, and stay ahead of the competition.
• Difficulty in understanding the impact that new AI technologies have across the organization.

Info-Tech's Approach

• Build awareness of the full scope of the issues in data, IT, and business.
• Determine data best practices by defaulting to AI, reimagining capabilities, evolving practice, and redefining the role and value of data. Define a strategic approach for collaborating and partnering with the business and building the right skills.
• Assess the current state of your data practice maturity. Define how it needs to change to meet demands and expectations, and revamp or create a practice strategy and roadmap to make it fit for purpose.

Info-Tech Insight

In the Exponential IT future, the role of data will shift from being a support tool for internal decisions, to becoming a driving force behind your core business strategy.

Define a Data Strategy to Power an Autonomous Enterprise

Exponential IT transformations cannot be achieved without data, analytics, and AI capabilities.

EXECUTIVE BRIEF

Introduction: What is Exponential IT?

• The technology curve has recently bent exponentially.
• Generative AI has been the catalyst for this sudden shift, but there are more and more new technologies emerging (e.g. quantum computing, 5G), putting significant pressure on all organizations.
• All IT leaders and organizations are at risk of falling behind if they do not adopt new technologies fast enough.
• Exponential IT is a framework defined by Info-Tech Research Group to instruct IT leaders across all IT domains on how to transform their organization and elevate their value creation capabilities, to close the gap between the exponential progression of technological change and the linear progression of IT's ability to successfully manage that change.

Your Exponential IT Journey

To keep pace with the exponential technology curve, adopt an Exponential IT mindset and practices. Assess your organization's readiness and embark on a transformation journey. This blueprint will help you build your roadmap to get there.

Your challenge

Data leaders will be required to develop new tactics to meet exponentially growing demand for data or risk being left behind.

The demand for data, as well as the increasing speed at which it can be processed and made available, will continue to grow exponentially.

With the advancement of AI technologies, organizations can now tackle more complex problems and build systems that will take advantage of real-time data from multiple. Organizations will start collecting new types of data (e.g. data from IoT devices, data generated by physical processes, synthetic data generated to support development of new products and services, etc.). These data sources will allow organizations to innovate and solve new, complex problems.

This exponentially growing demand for data increases the pressure on technology and data leaders to deliver the processes, capabilities, and technologies to service that demand; however, with only a few data leaders considered genuine business value generators by business executives, most of these leaders will be required to develop new tactics to meet these expectations or risk being left behind.

"The global data footprint will reach 175 zettabytes by 2025, with 90 zettabytes created by IOT devices."
Source: IDC and Seagate Data Age 2025

"72% of leading organizations note that managing data is already one of the top challenges preventing them from scaling AI use cases."
Source: McKinsey, 2023

"OpenAI's ChatGPT-3 was trained on around 570GB of datasets. Your AI use cases will demand significant quantities of quality data."
Source: BBC Science Focus 2023

Common obstacles

These barriers make this challenge difficult to address for many organizations:

Wasting time and resources on managing all data assets equally, missing business opportunities due to a lack of understanding of the most valuable data assets as the actual differentiator in value generation.

Inability of data & analytics team to meet business needs to innovate, grow, create differentiators, and stay ahead of the competition. Business views data, analytics, and AI only as a business enabler rather than as a product and driver in achieving the greater outcomes, e.g. shaping products and services and driving new business value. IT needs to work with business to understand how data needs to be managed to meet business need to innovate and grow.

Organizations are struggling to understand the impact that new AI technologies have across the organization, including business strategy, data, IT, and AI strategy. Organizations are wasting time and missing opportunities due to a clear lack of framework that should be used to assess how new technologies should shape the business model.

49.1%

Less than half of organizations report that they are currently managing data as a business asset.
Source: Wavestone, "2024 Data And AI Leadership Executive Survey."

89%

While 99% of respondents recognize the positive impacts generative AI can have on their organization, 89% of respondents report that their use of generative AI is being slowed.
Source: Elastic, "Global Generative AI Adoption Study," 2024.

The pains are felt across the entire organization

Business and IT transformations fail due to lack of business leadership.

Organizations are not prepared for the exponential speed and scale of AI innovation and the increasing amount of real-time data driven by AI growth, which makes data management and pipeline provisioning more complex and difficult to execute.

Business and IT transformations fail due to lack of business leadership. Organizations need to build an urgency and trust with business leaders. In most of the organizations, business stakeholders do not lead data and AI innovation and transformation, which results in the failure of the digital transformation efforts. This leads to wasted money, time, and effort.

• The greatest challenges to becoming data-driven are the function of culture, people, process change, and organizational alignment (77.6%) rather than technology limitations (23.4%) (Wavestone, 2024)

69% of respondents stated that employees in their organization struggle to access the data they need when they need it. (Source: Elastic, "The Elastic Generative AI Report," 2024)

Info-Tech's methodology to evolve data practice and redefine the value of data

Insight summary

Overarching insight

In the Exponential IT future, the role of data will shift from being a support tool for internal decisions, to becoming a driving force behind your core business strategy.

Phase 1 insight

When defining data & analytics practice value stream and capabilities, don't start with tools and technology. Think about practice as a business unit that delivers value to the organization. Define how the practice delivers value in the business.

Phase 2 insight

Lean into the skills gap. Data professionals need to understand the business as much as business needs to talk about data. Bidirectional learning and feedback improve the synergy between business and IT.

Phase 3 insight

Real-time enterprise is the data value's ultimate endpoint, which entails a complex level of connectivity between enterprise processes and real-time data that will drive exponential value and efficiency -- a digital twin of an organization (DTO).

Tactical insight

Break silos – business and technology is a single team. To take advantage of exponential growth, empower business stakeholders to take ownership of data, analytics, and AI capabilities to drive business value realization.

Tactical insight

Seeking input and support across your business units can align stakeholders to focus on the right data & analytics skills and build a data-learning culture.

Blueprint deliverables

Each step of this blueprint is accompanied by supporting tools and deliverables to help you accomplish your goals.

Additional tools and deliverables to be used in Phases 1-4.

Data & Analytics Practice Readiness Assessment Tool
A structured tool to help you assess the maturity level for each data practice dimension, then identify and prioritize Exponential IT initiatives and build a roadmap to ensure success.

Exponential IT Data Practice Strategy Template
A structured template to help plan your data practice strategy

Key deliverable

Exponential IT Data Practice Strategy Template

Demonstrate the need for Exponential IT in response to the rapidly changing technological landscape, then present the roadmap to achieve an Exponential IT organization.

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit

“Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful.”

Guided Implementation

“Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track.”

Workshop

“We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place.”

Consulting

“Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project.”

Diagnostics and consistent frameworks used throughout all four options

Workshop Overview

Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 8 calls over the course of 3 to 4 months.

Establish Baseline Metrics

Baseline metrics will be improved through:

1. Increased business and IT alignment
2. Defined data, analytics, and AI products that support business objectives
3. Lower the barriers to data-enabled innovation
4. Increased confidence in the successful delivery of eIT initiatives from a data & analytics perspective
5. Established appetite to adopt eIT in the data & analytics domain
6. Increased eIT project success rate
7. Increased business and IT alignment