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.
What is a Data Strategy?
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:
- Keep data safe and compliant
- Ensuring data security
- Supports decision making
- Identifying new business opportunities and exploiting these for the betterment of the business
- Improve team efficiency
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.
What is an Agile Framework?
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.
What are Agile Methodologies?
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:
- Teamwork is the most critical factor in agile methodologies. It is essential to have a team that has a subtle balance of developers, QA testers, and creative designers. When you build the right agile team, it ensures stability. Reason: They will be able to work with each other well.
- Unless the organization – including everyone from the top brass to the lower employees – is driven by metrics, agile methodologies can’t work for the benefit of an organization. Result: Every metric needs to be tracked and recorded – customers returning or churning, sales increasing or dipping, every metric is documented.
- Communication is another critical factor in agile methodologies. For a team to succeed, it is crucial to have effective communication.
- build constructive relationships with their colleagues and strengthen them as well,
- foster constructive transparency in the working style,
- create an organization that has productive culture,
- support innovation
- collaborate with other teams and partners to achieve the desired goals
Blending Agile Methodologies with Data Strategy
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.
Seven Main Elements of Data Strategy
To build a robust data strategy, organizations need to ensure these seven main elements:
1. Understanding business goals
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.
2. Data sourcing
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
3. Tech stack
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?”
4. Turning data into insights
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.
5. People and processes
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.
6. Data governance
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.
7. Data strategy roadmap
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.
How Organizations can Build an Agile Data Strategy Roadmap
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:
- Your enterprise data strategy should cover how you will collect the data
- Next, it should clearly define how to store the collected data
- Integration of that stored data is the third key component of your enterprise data strategy
- Your enterprise data strategy should also determine who can access the stored data
- Last but not least is the governance of that data. Data governance is essential because it helps organizations trace the source of the data. Additionally, there is no tool for data governance; it is a people’s work and needs to be done manually without errors.
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:
- Definition of how the workflow is managed and what the criteria is for measuring success
- Alignment of the technology team with the business perspective to achieve the desired goal
- Important data sources and how an organization can manage its hybrid data
- Set of processes and tools for analytics, visualization, and data management
Data-Driven Strategies for Improved Customer Retention
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.
- Use AI and machine learning to understand what customers want and like. Machine learning helps you understand the behavior pattern of customers and gives you insight into their churning.
- Another reason for customer churning includes a lack of clarity in your brand’s voice and buyers' persona. Data can help you figure out where you are lacking and how you can address those areas to build stronger relationships with your customers. You need to engage with your customers through phone, text, or email and immediately address their concerns if any.
- An RFM model called Recency, Frequency, and Monetary Model can help you convert your data into sales. It enables you to understand your customers' psyche and how well you can cater to them to keep them coming back to you. You keep collecting the behavior data of your customers and leverage it to increase retention.
- Niche down to one or two products at a time. Yes, you want to be a one-stop shop for everything your customer may need. However, when you niche down to fewer products, your customers get the message that you are an expert in your products and services and will come back to you for more.
- Each customer is different and unique, with varied tastes. So, customize your offers as per your individual customer’s choice through data. Data collected about how they shop, when they shop, what they shop for, and what products they are looking for will help you gain deeper insights into your customers’ behavior and help you draft a more personalized offer to lure and retain them.
Conclusion
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.
