Business activities require decisions. People perform analysis to make decisions based on available information, including the proverbial “gut feeling.” In supporting decision-making, enterprises have spent decades trying to shape a ‘management by facts’ approach with the aid of analytics and information management platforms.
Now we operate in a digital world with an abundance of data. Automation and artificial intelligence, powered by cloud processing, help enterprises to convert data into meaningful insight faster and more efficiently. This is the brain of the digital enterprise or digital intelligence.
Intelligence has been used internally to help enterprises monitor and optimize performance. Today’s forward-thinking business leaders see far-reaching advantages with digital intelligence because it can effect change outside the enterprise on a greater scale. It’s the ability to deliver truly relevant customer experience, create new business models with partners, and leverage supplier’s information to achieve market growth. Futuristic analysts use data and advanced analytics to foresee risks and opportunities associated with global situations impacted by economics, politics, and/or society.
Although enterprises strive for digital intelligence, many are not achieving their data and analytics program goals. An alarming finding comes from a NewVantage Partners’ report, 2019 Big Data and AI Executive Survey, where 64 c-level technology and business executives, representing very large corporations, indicated that firms identifying themselves as being data-driven have declined in each of the past 3 years – from 37.1% in 2017 to 32.4% in 2018 to 31.0% in 2019. As boards try to push for data-centric organizations, this should be a concerning statistic.
Why Maturity Levels Matter
In reaching digital intelligence that aligns with business expectations, the enterprise has a complex journey ahead. For instance, to build such information management capabilities, organizations need to break data silos, ensure the credibility of data solutions, modernize their data estates, and enable broad-based access to data.
As organizations navigate the complexities of a modern data and analytics program, it can do more harm than good by focusing on part of the analytics workflow that are not aligned to their maturity level. For instance, many organizations look at each data problem individually, which can compound the challenge across the data landscape. It is often mistaken as a mere technology problem or lack of use-cases articulation.
With a plethora of technologies and processes readily available, it is vital for enterprises to understand their maturity levels in terms of organization structure, service methodologies, data governance techniques, and identify the best-fit methods. Such an understanding of prevalent maturity becomes the basis for transformation and the baseline to measure the transformation against.
The Journey to Digital Intelligence Starts with Data & Analytics Maturity
With all its potential, analytics (and information management) is one of the most important business initiatives to positively impact an organization’s health. For example, a CIO research conducted by TCS shows that data related to customer experience and purchase history data are viewed as critical to future growth. Seventy-one percent of CIOs rate data on what customers are saying about their firm in online channels, including social media sites, and product and service review sites, as highly or extremely important. Data on what products and services customers buy from their company (or show interest in buying) also is vital, with 63% rating it as highly or extremely important.
As enterprises try to tap into the opportunities, it is best to start in small, pilot phases. Otherwise, the endeavor will be a major failure. The key with every journey is to have a clear understanding of the current condition and then define a path that includes key milestones along the way.
To optimize the health of your digital intelligence, it is important to follow a journey through five key maturity levels
1st Maturity Level: Tactical Capabilities
2nd Maturity Level: Strategic Visibility
3rd Maturity Level: Operational Excellence
4th Maturity Level: Ecosystem Intelligence
5th Maturity Level: Universal Intelligence
The five levels provide organizations a way to comprehend its maturity level of analytics and information management initiatives, along with its supporting technology backbone, to yield business benefits with certainty.
The first three levels are about building the foundation to measure, analyze, and govern the performance of the Enterprise Intelligence. The fourth and fifth maturity levels build on this to generate insights about the known ecosystem within which the organization sits, and eventually bring visibility of unknown but relevant realities.
Level 1: Tactical capabilities
In this level, the enterprise departments/divisions are working in isolation and managing their own tactical capabilities. As a result, the enterprise has pockets of excellence.
Individuals in the department tend to follow the same culture, using Excel and MS Access to store and access their own data and build their own reports. There are multiple copies of the same data across the enterprise leading to high cost, less manageability, and eventually low trust on the data. Additionally, uncontrolled and unstructured (e.g. reports in PowerPoint or Word or Excel) versions of data outputs are generated and distributed as well. The obvious outcome is the “individual versions of the truth”.
Level 2: Strategic visibility
As organizations move from Level 1 to Level 2, they are essentially working towards end-to-end enterprise intelligence. Here, an organization focuses on strategic visibility with analysis of the integrated information or “authorized source of truth”.
Usually, this is achieved through an Enterprise Data Warehouse (EDW) or Data Lake with breadth (across the enterprise functions) and depth (granularity of details) of the historical information to enhance the analytical capabilities such as ad-hoc analysis and data mining. To be successful, strong governance spanning across the key focus areas, namely people, process, technology, and data, is required. However, most enterprises struggle to build it due to its complexity.
Level 3: Operational Excellence
The intent of the third level is to have ‘right information available to the right person at the right time’. The third maturity level is about operational excellence through strategic visibility and tactical capabilities with in-time information analytics.
When coupled with end-to-end processes, linking strategic KPIs to tactical KPIs to operational KPIs establishes a strong and transparent performance-driven culture. Here automating processes helps to accelerate the information capture and deliver KPIs faster.
Level 4: Ecosystem Intelligence
The fourth maturity level combines Performance Intelligence and Relationship Optimization together to establish ecosystem intelligence. Enterprises are a part of the larger ecosystem and they need to acquire the ecosystem intelligence by managing and then optimizing the relations with various entities in the ecosystem. These entities can be customers, suppliers, competitors, regulatory bodies, sister-companies, subsidiaries as well as individuals, such as consumers and employees. Our CIO research indicates that companies are looking for productive ways to share customer data with strategic partners. More than half of CIOs (54%) say their firms have strategic partnerships with external parties to use customer data to identify potential customers who may be interested in their companies’ offerings and to help them convert prospects into customers.
Level 5: Universal Intelligence
The final maturity level focuses on applying universal intelligence to institutionalize innovation by correlating performance, relationship, and risk for the enterprise to achieve the state of self-optimization with the backbone of an intelligence center. Through universal intelligence, enterprises can facilitate business excellence, driving innovation, and reimagining business, models.
Data and Analytics Stewardship
The enterprise needs to build foundational Enterprise Intelligence capabilities to evolve from merely managing tactical needs to realize strategic potential while handling operational excellence. After strengthening the self-performance, the enterprise needs to continue learning about the ecosystem and its players and optimize the relationships with them.
Only then, should an enterprise find the right balance of human-centric and machine-centric decision making to institutionalize innovation, which is key to manage self-performance, ecosystem strength, and universal risks.
The initial starting point on a modern data and analytics journey is to evaluate data and analytics initiatives holistically and assesses the data maturity level by how the data is managed and used across the organization. This step helps to identify gaps in the existing data and analytics programs and becomes the basis for developing specific approaches to ensure a healthy enterprise.
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About the author(s)
He heads the Data and Analytics Practice under Analytics and Insights Unit at Tata Consultancy Services Ltd. with the prime focus on strengthening solution delivery capability through integrated solutions and offerings (spanning BI, Information Management, Big Data, Business Analytics, and Artificial Intelligence), strategy and architectural excellence. Along with the thrust on bringing industry experience into analytical computing, his expertise span across Business Strategy, Practice Management, Corporate Research & Innovation, Consulting, Presales, and Coaching.
He has more than 25 years of experience in the field of Data and Analytics. He has been one of the founding members of BIPM Practice in TCS. He has been instrumental in accelerating the competency development and managing operations for bringing substantial revenue growth, analysts, partners, and institutional relationship and customized services under BIPM. He also led the growth in Big Data and Analytics under Digital Enterprise Services & Solutions Unit. He has conceptualized and built TCS frameworks and solutions in Business Intelligence and Data Warehousing branded as BIDS™, overall next-generation Data & Analytics Target Operating Model (DATOM™), Digitized Information Management Ecosystem (DIME©), Decision Fabric™ (Solution for Contextual Industry Offerings using different disciplines of AI components) and Data & Analytics Estate Modernization. He has 2 patent granted and 3 more submitted in the areas of AI and Information Management.
He has led many Data and Analytics solution deliveries in the domain of supply chain management, telecommunications, and financial services, which include Data and Analytics initiatives for Walmart, Bank of America, Reserve Bank of India, British Telecommunications and Sony Pictures.
He holds a Bachelor of Technology degree in Electrical Engineering from IIT Bombay.