Data Strategy

  About the Author

I am Bighneswar Swain currently working at Tata Consultancy Services as North America Lead for BIPM from A&I Data services. 

I finished my MBA from Amity University, MCA from NIT Rourkela and Initiation & Planning of project from University of California. 

Abstract

Data is the lifeblood of an organization. Data strategy is the process of planning or creating strategies/plans for handling the data created, stored, managed and processed by an organization. It is an IT governance process that aims to create and implement a well-planned approach in managing an organization's data assets. 

Data Strategy describes a “set of choices and decisions that together, chart a high-level course of action to achieve high-level goals.” This includes business plans to use information to a competitive advantage and support enterprise goals.

1. Data Environment

Data is the lifeblood of an organization. Organization sells data as finished product, transformed into insight and wisdom. When serving their clients and associates, data becomes shared knowledge for new and interesting business acumen. This is the digital brand & reputation in the marketplace for the organization. 

Consequently, data is one of the Org’s greatest assets -- critical to running the business and effectively serving the clients. Almost every org’s data grow at an average of 30% per year. Organization acquire data from clients, purchase third party data, utilize public open source data, and produce their own data. In effect, the sole purpose of technology infrastructure, on the other hand is to serve as a pipeline to transfer in, across and out of the organization. 

Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analysed or used at all. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data. 

Org expects their associates to be able to access data anywhere, anytime, securely and on any device. Associates spend valuable time searching for or recreating data or purchase third-party data which they didn’t know they already had, at great expense to the Org. Organizations are operating in a siloed, fragmented environment for far too long. To be a truly digitally-enabled, Org must change their culture.  

2.   Why do Org need a data strategy?

 According to the DAMA International Data Management Book of Knowledge 2.0 (DMBOK2), Data Management is: “The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”  

The DMBOK2 definition of Data Strategy: “Typically, a Data Strategy requires a supporting Data Management program strategy – a plan for maintaining and improving the quality of data, data integrity, access, and security while mitigating known and implied risks.  

The rationale for developing a data strategy is to make sure all data resources are positioned in such a way that they can be used, shared and moved easily and efficiently. Data is no longer a by-product of business processing – it’s a critical asset that enables processing and decision making. A data strategy helps by ensuring that data is managed and used like an asset. It provides a common set of goals and objectives across projects to ensure data is used both effectively and efficiently. A data strategy establishes common methods, practices and processes to manage, manipulate and share data across the enterprise in a repeatable manner. A data strategy provides the basis for all enterprise planning efforts connected to data-related capability. Finally, a data strategy is the tool that allows for unification of business and IT expectations for all enterprise data-related capabilities.  

Chief Data Officer will be actively developing and implementing initiatives and projects to provide effective data management across the org. A data strategy establishes a road map for aligning these activities across each data management discipline in such a way that they complement and build on one another to deliver greater benefits. Data strategy, driven by the Org’s overall business strategy and business models, matched against emerging technology, will help to guide in common purpose and direction. It will point toward where improved capacity needed, technology, processes and policies to manage the complexity of the data on which the org depends. The data strategy provides a common framework for the cost-effective sharing data across the org, while ensuring access control, security, respecting privacy and protecting the appropriate use of information. 

3.   Data Strategy

To enable associates to be effective, and to run the business efficiently and manage costs, we need to interact the data and information needed to run the business. Through new policies and behaviour changes across the org, we will:  

·      Remediate the data which don’t needed

Data remediation is defined as the intent of an organization to get past “keeping everything forever.” This “digital landfill” generates significant unnecessary risk and cost, while it hampers productivity and business growth. A goal, then, of data remediation, is to minimize “run the shop” costs to increase investment in “grow the org” activities. Some data should be retained ---

(a) data on legal hold

(b) data designated as official “records” in accordance with the organizations, and

(c) other data that has current business value.

Studies and analysis by the Compliance, Governance and Oversight Counsel indicate that, typically, 1% of corporate information is on litigation hold, 5% is in a records category, and 25% has current business value. This means that 69% of information in most companies has no business, legal or regulatory value. Industry largely characterizes this as ROT -- the low value, redundant, obsolete, trivial data. 

·      Master the data that matters

To effectively serve clients and run the company, one must have one source of truth --- high-quality, authoritative data on people, clients and org. An effective data strategy means that important data assets are managed properly -- and that we have, for example, a single, trusted view of a client’s data across our systems and processes. A Forrester research report indicates that organizations believe that over 27% of their revenue is wasted due to inaccurate master data. A strong master data capability will provide daily operations, and data asset management lifecycle and integration for all datasets under the control of Chief Data Officer.  

·      Index the most valuable data

One must identify and inventory the most important data -- client, internal, external data sets - so associates can find what they need to do their work. As data stores grow and become more complex, it is critical to index the data to allow for near instant, and accurate, search results. 

·      Expose data to use, securely and through self-service

One must provide the leadership and associate with better access to the data they need to effectively serve clients and run the org. Timely access to critical data provides a tremendous competitive edge and separates the winners from the losers in today’s information economy. Yet all too often knowledge workers fail in their quest to obtain the data they need. The amount of time wasted in futile searching for vital data is enormous, leading to staggering costs to the enterprise, as well as to lost revenue opportunities. 

4.   Organization Goals behind Data Strategy

 The objectives reflect the recurring themes applicable to internal and external stakeholders.

 ·      Build data capacity and capability that effectively manages data across the entire life cycle --- acquire, create, store, access/use, share, archive dispose/destroy. 

·      Provide a common framework for the cost-effective sharing of data across organizational lines while respecting the security, privacy and appropriate use of that information. 

·      Maximize the use and value of data and facilitate the reuse of data. 

·      Provide greater insight and innovation from enterprise data.  

·      Promote greater confidence in enterprise data for better, faster decision making.  

·      Address data accessibility, duplication, awareness and quality issues. 

·      Establish strong, central data governance. 

·      Develop a data-driven data-oriented culture change which introduces data into the everyday conversation in the workforce and supports data sharing as central to org success. 

·      Facilitate a more data informed and empowered workforce. 

·      Improve data infrastructure by restructuring the data architecture and redesigning applications in order to reduce duplication and improve data integrity, accessibility, reporting and capacity for analysis. 

·      Improve data collaboration to ensure better use of the increasing numbers of datasets available. 

·      Make data available to all who have an authorized need for it. Collect data once and use it many times - share it. 

·      Actively contribute to priority initiatives and strategies 

·      Capitalize on emerging data related technologies, particularly artificial intelligence, data analytics and big data. 

·      Reduce data related risk, including increased security and privacy of data  

·      Develop better visibility in to the true cost of the data  

·      Reduce data related operational and administrative costs. 

·      Drive data related innovation and collaborate with the lines of service to incubate promising data services that could lead to market leadership. 

·      Create data policies that protect clients and org, while enabling forward-leaning practices that advance leadership in the data age. 

·      Provide more user-friendly was to find, access, share and use data. 

·      Reduce the incidence and retention of ROT (redundant, obsolete, trivial) data. 

·      Identify authoritative data sources and establish systems of record for priority classes of data. 

·      Optimize third party data management and spend 

·      Constitute and govern a system of data stewards who will have several responsibilities, including acting as a point of contact for data related questions, ensuring appropriate data lifecycle, identifying data gaps, suggesting data process improvements, and collaborating and facilitating with other stewards’ ideas for improving integration and coordination of data activities 

5.   Risk vs Outcomes

 Data strategy must be one that both mitigates risks and improves business outcomes --- one that balances “Défense versus offense.” 

There are considered trade-offs between “defensive” and “offensive” uses of data and between control and flexibility in its use. The trade-off helps design data management activities to support the data strategy. Data Défense and offense are differentiated by distinct business objectives and the activities designed to address them.  

Data Défense is about minimizing downside risk. Activities include ensuring compliance with regulations, reducing cost, protecting privacy, using analytics to detect and limit fraud, and building systems to prevent theft. Defensive efforts also ensure the integrity of data flowing through internal systems by identifying, standardizing, and governing authoritative data sources into a “single source of truth.”  

Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction. It typically includes activities that generate client insights such as data analytics, identifying new ways to use data, facilitating data as a fast and accurate decision-making tool, and improving the productivity of associates by making data easier to find and use. 

Offensive activities tend to be most relevant for customer-focused business functions such as sales and marketing and are often more real-time than is defensive work, with its concentration on legal, financial, compliance, and IT concerns.  

Every organization needs both offense and Défense to succeed, but getting the balance right is very important, as the two aspects can compete for resources and funding. For example, in heavily regulated industries such as health care, data quality and data protection are critical, those these organizations would incline toward Défense. Retailers are less regulated and must react quickly to competitors, so they would emphasize offense. Financial services are heavily regulated, but they operate in dynamic markets and competition, so they would tend toward and equal balance of Défense and offense. 

This balance creates an additional trade-off are between standardizing data and keeping it more flexible. The more uniform data is, the easier it becomes to execute defensive processes, such as complying with regulatory requirements and implementing data-access controls. The more flexible data is and the more readily it can be transformed or interpreted to meet specific business needs, the more useful it is in offense. 

A data Défense strategy will emphasize security, privacy, quality, compliance, governance, standardization and a single source of truth. Alternatively, an offensive data strategy will stress improved competitive position, analytics, modelling, flexibility, enrichment and multiple versions of truth. 

6.   Challenges

 In any organization there are many challenges that can impact successful execution of a data strategy. Some of these include:

 ·      The sheer volume of data, and its uncontrolled growth, thus increasing the difficulty to aggregate, manage and create value from data. 

·      Increasing complexity of data, applications, and storage devices increase the dependency on agile technology, and increase the management workload. It inhibits the ability to find and retrieve data. 

·      Cultural challenges, whereby different parts of the organization, and different staff, all have different beliefs regarding the value of data, its use and its ownership. 

·      Difficulty in determining the true cost of data, particularly in assigning a total cost of ownership including indirect costs, vs. easily identifiable direct costs. This can inhibit the development of an effective business case and return on investment analysis for data initiatives. 

·      Difficulty monetizing data, particularly when data is not sold directly in the marketplace. This inhibits prioritization of data initiatives and competition for scarce resources. 

·      Data is decentralized and siloed. Data is created, stored and managed by different technologies and applications. Data ownership is fragmented. 

·      Dichotomy of data flexibility vs data standardization increases the complexity of data architecture and management policies. 

·      Unequal priorities across the lines of service, and the internal functions, reduces the ability to share data and derive maximum value. 

·      Immature internal processes, and less efficient technologies, can impede the ability to facilitate and monitor compliance with data related policies and best practices. 

·      Evolving compliance and increasingly sophisticated data breach threats dramatically increase complexity, risk and cost.

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