The Economist published a statement that reads, “The world’s most valuable resource is now data.” [1] Historically been treated as just a piece of a technology project and not a corporate asset. Many business executives assume that conventional application and database planning endeavors are adequate to resolve current data issues. But business data reserves have grown in volume and subject area variety, necessitating the need for a data strategy to inform how a company’s information assets can be managed. Read on as we take you through how to develop and implement a data strategy framework.

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Today, many industries are adopting the use of big data to help streamline processes and aid in strategic decision-making. However, despite the hefty and sustained investments in data management, many companies still experience a wide range of data challenges. For example, over 50% of the data gathered by organizations goes unused [2]. And in a survey conducted by NewVantage Partners, almost 99% of CEOs intend for their enterprises to be data-driven. But only 26.5% have actually accomplished this goal [3].

Data: Past and present

Not so long ago, data was thought of as a byproduct of a business transaction or activity. Once the process was done, the data had little value afterwards because there were one or two processes that required access to the data for follow-up purposes. But these were often once-in-a-while activities. For example, writing special reports, audits, and customer service operations.

data strategy framework benefits

Presently, business is rather different because organizations have realized the value of data. A 2013 report from Bain & Company[4] established that companies that successfully leverage their data are:
• Twice as likely to be in the top quartile of financial performance within their sectors
• Five times more likely than market peers to make decisions quickly
• Three times more likely to carry out decisions as intended
• Two times more likely to use data very frequently in decision-making

It’s common for application data to be shared by as many as 12 other systems. Even with the recognition of the value of data over the past years, not many organizations have fine-tuned their strategies for capturing, sharing, and handling business data assets.

This sort of behavior typifies an archaic, underlying assumption that data is just an application byproduct. Companies, therefore, need to formulate data strategies that correspond to today’s realities. To create an effective data strategy, organizations need to take into account their business and technological goals.

Read more about Introduction to big data platforms

Data strategy defined

The concept behind creating a data strategy is to ensure the effective and efficient use of data resources. As mentioned earlier, the data is no longer a result of business processing. But rather an important asset that powers decision-making and processing. The aim of a data strategy is to ensure that data is no longer handled as an application byproduct but as a corporate asset.

It supports the application of data to achieve business objectives by setting up common methods and practices to handle, manipulate, and share data across various company departments repeatedly.

data strategy framework Source: dataversity.com

Most enterprises may have instituted several data management activities such as data governance, metadata, data migration, master data management, modernization, data quality, data integration, and so on. A data strategy builds a road map to align these processes across each data management field.

Consequently, they are able to complement and build on each other to generate greater benefits.
Data strategies are complex due to the many moving parts. So, creating one may not be easy. That said, here is a framework to help you build a robust data strategy:

A data strategy framework: 5 key fundamentals

ESTABLISH BUSINESS REQUIREMENTS

The first step is to gather and document the business requirements. This helps navigate the first hurdle faced by many IT projects: Understanding what the company is trying to achieve. For instance, what specific business needs do you want to achieve with data? Data must answer this question if it is to generate real value.

To define the business requirements, you need to identify the champion, all stakeholders, and SMEs in the company. The data strategy’s champion is the senior executive who will drum up support for the investment. Equally, stakeholders and other SMEs will embody specific departments or roles within the organization.

The next step is to outline the strategic goals. The stated objectives both at the company and department level must sync up. These objectives are typically gathered via an interview process that begins at the senior management level and trickles down to the department’s supervisors. This allows you to establish various things, including:
• What do leaders aim to measure?
• What they are striving to improve
• Questions they desire answers for
• Key Performance Indicators (KIPs) to answer those queries

Now explain the role of data in achieving the business requirements or goals. Let’s assume one of your organization’s main objectives is to boost annual profits by 20%. You can explain how your data strategy will play a part in that.

Getting the company’s decision-makers involved will help you build a better data strategy overall. And getting their backing and support at this early stage is crucial.

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DATA ARCHITECTURE

Next, you need to outline your data architecture. With data architecture, the objective is to understand the structure of your data. Otherwise, you will be unable to devise a blueprint on how to manage your business data.

Data architecture is the cornerstone of any data strategy. [5] It is a unique blueprint that defines the “how” when developing and executing a data strategy. It also seeks to standardize how companies acquire, store, handle, use, secure, and combine data with various applications and data warehouses.

data architecture

Data architecture is basically like a master plan containing instructions and standards that describe actual data flow in an organization and how to control it.

Here are some of the questions answered by data architecture:

• What type of information should I collect and from which sources?
• Where will that data be kept?
• How to process the data effectively and efficiently
• What are the methods of capturing data?

A well-defined data architecture promotes an environment that:

• Guarantees superior data quality
• Enables data integration from contrasting sources
• Offers a single source of truth about the organization
• Allows businesses to generate new insights and make decisions backed by data
• Ensures data security

ASSIGN OWNERSHIP

A survey of 3,000 companies by Forrester discovered that 45% of companies had appointed someone to manage their data governance strategy. [6] The owner of your company’s data strategy is the chief data officer or data chief. The main roles of the data chief include:
• Implementing a data strategy framework
• Helping the organization leverage data to innovate
• Setting KIPs
• Compliance with data laws

It’s natural to have different owners for various sections of the data since various teams need to feel some sense of data ownership. This helps to bring some level of accountability, especially when something goes wrong.

data chief's role

For instance, an individual or team can be responsible for the reporting data while another takes care of operational data. You may also need to allocate data ownership responsibilities at various phases in the pipeline. The business intelligence team may own the data at a specific stage before passing it on to the data analysts.

You also need to assign ownership over the different tools being utilized in the data stack. The individual appointed to manage each tool should have expertise in that technological tool. That way, they can address specific questions about the tool. Take the example of Google Analytics. The person owning this tool should be at least working with it daily. That can be someone in your analytics or marketing team.

DATA GOVERNANCE

Data governance is a collection of guidelines and regulations that explain how data will be gathered, stored, and secured with the utmost accuracy and quality. Simply put, data governance stipulates the threshold that an individual must meet if they want to use the source of truth data.
The prerequisites may include:
• Satisfying coding standards
• Having a specific number of reviewers
• Adhering to a certain documentation process

A cross-functional data and analytics environment needs a data governance program. This is because it makes sure that data is useful, high-quality, reliable, secure, and accessible. [7]

It also helps your data strategy to tackle the organizational culture beyond obvious operational technologies and handles matters touching on roles, responsibilities, and compliance.

You can approach data governance from two viewpoints:
Centralized approach: It’s more traditional. It imposes strict guidelines on who can access what data and how they can utilize it.
Democratized approach: Provides all your business users with access to big data and the ability to generate business analytics, leading to faster decision-making.

Overall, data governance holds the teams, processes, and technological tools accountable by answering the following questions:
• What makes up data?
• What is the data lineage?
• Who cares for and manages which data?

REVIEW REGULARLY

A data strategy is not a short-term initiative but a long-term collection of goals. So it’s natural to set a multi-year set of milestones and identify quarterly or biannually deliverables. This allows the strategy to go through assessment and measurement on a continuing basis.

For example, suppose you introduced a new ingredient in your product and are trying to gather customer feedback on it. In that case, you may need a more defensive approach to data strategy for purposes of securing customer data. But if your organization grows massively, you may have to drop the centralized system and shift towards a distributed one.

data strategy

Even in the absence of changes in your company’s operations, you may still need to perform some reassessment and tweaking. Here are two tell-tale signs that indicate the need to appraise your data strategy:
• Things are taking too long, leading to frustration
• The data is not trustworthy or reliable

A good rule of thumb is to examine your strategy at least every 6 months or a year. Talk to business leaders, IT guys, and your data teams to get a feel of everyone’s opinion on everything and decide what adjustments are needed.

Final thoughts on data strategy framework

Data strategy is not reserved for big companies with tons of data. Small businesses could also benefit from building a data strategy that can help them scale. A data strategy is a requisite tool as your enterprise looks to become data-driven. It is not a once-and-done project but a long-term initiative that adopts a holistic view of people, processes, and technology.

That said, a data strategy framework will help you build and implement your data strategy by creating a roadmap around data management. The process of developing a data strategy varies from one organization to another. Your company size, data maturity level, and industry all determine the steps you take. As you devise your data strategy, keep in mind the 5-step data strategy framework explained above. And, of course, remember about data analytics consulting, too!

 

[1] Economist.com. The world’s most valuable resource is no longer oil, but data. URL: https://www.economist.com/news/leaders/21721656-data-economydemands-new-approach-antitrust-rules-worlds-most-valuable-resource. [Accessed: 02-Oct-2017]. Accessed April 27, 2022
[2] Priceonomics.com. Companies Collect a Lot of Data. URL: https://priceonomics.com/companies-collect-a-lot-of-data-but-how-much-do/. Accessed April 27, 2022
[3] Businesswire.com. Data and AI Executive Success. URL: https://www.businesswire.com/news/home/20220103005036/en/NewVantage-Partners-Releases-2022-Data-And-AI-Executive-Survey. Accessed April 27, 2022
[4] Meda.bain.com. Big Data: The Organizational Challenge. URL: https://media.bain.com/Images/BAIN_BRIEF_Big_Data_The_organizational_challenge.pdf. Accessed April 27, 2022
[5] Mckinsey.com. How to Build a Data Architecture to Drive Innovation Today and Tomorrow. URL: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/how-to-build-a-data-architecture-to-drive-innovation-today-and-tomorrow, Accessed April 27, 2022
[6] Forrester.com. You May Not Need CDO But Wouldn’t You Want to Improve Your Odds of Succes. URL: https://www.forrester.com/blogs/15-08-20-you_may_not_need_a_cdo_but_wouldnt_you_want_to_improve_your_odds_of_success/. Accessed April 27, 2022
[7] Analytics8.com. Creating a Data Governance Program Does Not Have to be So Hard. URL: https://www.analytics8.com/blog/creating-a-data-governance-program-does-not-have-to-be-so-hard/. Accessed April 27, 2022

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