Picture this: On average, organizations lose about $12.9 million due to poor data quality. While the short-term impact is on the revenue, the long-term effect of insufficient data is poor decision-making and data ecosystems’ complexities.
The solution?
A robust strategy for the data.
Yes, strategy for data or data strategy to be more precise. Organizations have a business strategy to run and manage their business. Companies demand a data strategy to address the continuous data flow. This data comes in structured, unstructured, and raw.
Every business generates tons of data every nanosecond, and organizations need a robust data strategy to make sense of that data.
A data strategy is an amalgamation of rules, tools, and processes that help organizations define how to manage and analyze the data and use it to get valuable insights. With a data strategy in place, businesses can figure out the reasons for common business problems like customer churning and decline in sales, among other things.
There are numerous benefits of having a robust data strategy and why your organization needs one:
Data strategy itself is a robust practice to solve data-related problems. Yet, when an agile approach is applied to a data strategy, it becomes a winning combination for organizations.
Before delving into the hows, let’s understand what an agile framework is and how organizations can apply it to their data strategy.
In simple words, the agile framework is a unique approach organizations apply to plan, manage, and execute their work. It is a set of practices to achieve the desired goal. There are majorly two categories of agile frameworks – frameworks designed for teams and frameworks specifically designed for organizations to practice agile at scale.
To understand agile methodology in data strategy, let’s first understand what agile methodologies are.
Agile methodologies are a group of frameworks that help organizations repetitively manage their projects. Agile methodologies focus on sending the message out, compared to traditional methods of collecting requirements and then acting on them.
Agile methodologies create opportunities for continuous assessments of the direction of project development. While there are numerous examples, the few common examples of agile methodologies include Kanban, Scrum, and extreme programming, aka XP.
But how do agile methodologies align with data strategy? Before we address this question, remember these three crucial elements of agile methods:
We have seen what a data strategy and agile methodologies are; now, keeping both in mind, let’s blend both features and figure out a data strategy with agile methods that can drive growth in an organization. That is keeping the prominent factors of agile methodologies and data strategy and how it benefits an organization. It can be concluded that when agile methodologies blend with the right data strategy, the result can be spectacular for an organization.
When aligning the agile framework with data strategy, it is essential to remember that there are no specific steps. The process starts with obtaining the data, scrubbing it clean – implying separating the valuable data from the trash – and then presenting it to a team that will explore it to figure out either a solution to a problem or even a problem at times. Then comes the data modeling and interpretation step, where the data is studied to reveal a pattern that will help solve the business problem.
How all this aligns with data strategy is by planning and prioritizing the data obtained. Some teams are constantly researching and developing ways to predict the data accurately.
Agile methods are not just for software; they are for more. Companies must grasp the seven parts of data strategy to attain the desired goal and achieve the desired outcome. If they don't, everything could fail.
To build a robust data strategy, organizations need to ensure these seven main elements:
The first element of a data strategy is understanding business goals. As an organization, it is essential to understand your business's requirements. Requirements here imply your business’ specific goals that it wants to achieve within a given period.
The next element of a data strategy is how to source and gather data for insights. This element identifies the places from where you should source the data and use it for the wisdom that will help in critical decision-making
The next element of a data strategy is the recognition of the technology infrastructure required to build a successful data strategy. While a technology stack is necessary to create a data strategy, it is advisable not to get stuck. This is where your organization addresses some critical questions like “Does your operational system support your data analytics?” or “Does your organization have the required technical skills and infrastructure to build a complex data strategy?”
Organizations may be collecting vast amounts of data from multiple sources, but it is useless if they cannot make any sense of that data. A data strategy offers recommendations on applying analytics to gain insights from the information that can be critical for the business. And tools like data visualization make it easier for organizations to understand their data and thus easy to interpret.
No matter how much the technology evolves and develops, the core of any organization, especially a data-driven organization, will always be its people and processes. A data strategy will help organizations analyze whether they need more skilled people, if the methods are up-to-date or if there is a need for change.
The next main element of a successful data strategy is data governance. Think of data governance as the oil that fuels the machinery that is the analytics practices and aids organizations in sharing the data. Data governance in organizations will ensure that the right people have the proper access to the correct data. It will also help in defining the exact source of data origin.
The last and central element of the data strategy is the roadmap, which is essentially a culmination of an organization's work to deploy a data strategy.
A data strategy roadmap is your guide that will help you stay on track and help you record where you are slipping and how you can stay on track. It can also help organizations decipher any delays in the process and how to tackle them.
Now that the elements of a data strategy are clear let’s understand how organizations can build a data strategy roadmap with an agile framework.
An agile data strategy roadmap includes the main elements of a data strategy discussed above and there here are five crucial components that your enterprise data strategy should cover:
Note that a a data strategy roadmap is a plan that will help your organization achieve the data strategy goals. It defines the phases and how each process, like ideating, designing, implementation, and governance, will be carried out to reach a successful outcome. It also helps list the KPIs of the team members, to measure success.
An agile data strategy roadmap is a guide that helps implement a data strategy, which is flexible and scalable.
It blends short-term projects with long-term planning, and all the stakeholders are kept in the loop through regular iterations and reporting. This supports the alignment of quick wins with strategic goals.
You might wonder now what the best framework for enterprise data strategy is. Here are some aspects that should be included in your framework:
Customer retention is one of the major concerns of many businesses, especially eCommerce. Choices are galore. Even a loyal customer can churn at a moment’s notice if the services are not up to their expectations.
Let’s take customer retention as an example to see how data-driven strategies can be used to boost results.
Data-driven strategies are based on actual numbers and facts and not guesswork. Result: the business knows what a customer is looking for, what they like, and how best the company can offer them.
Here are the top five data-driven strategies that can have a positive impact on customer retention.
By bringing an agile approach to a data strategy, organizations can stay abreast with the latest trends in the market. But that is not all; organizations gain numerous advantages by blending the agile approach and their data strategy, including keeping data safe and compliant, ensuring data security, supporting decision making, identifying new business opportunities and improving team efficiency. In other words, agile data strategy is the way to go for businesses if they wish to stay ahead of their competitors and stay in the market.