The quest for Data Agility
The world of Tech in general and that of Data, in particular, is jargony. Everyday, new concepts arise. This plethora of terms makes it difficult for the uninitiated to navigate the world of Data.
Here is a new phrase: Data Agility. What concept lies behind this term that — As we will see further in this article —is not devoid of interest for businesses.
What is Data agility
Data agility is not another fancy term for Data governance. Although related, both concepts should not be mistaken. An organisation can be well-equipped with a robust and functional Data governance framework and still fall short on Agility. Data agility refers to an organisation’s ability to quickly and effectively respond to changes and opportunities by leveraging its data resources. It involves the capacity to adapt, innovate, and make informed decisions in a timely manner based on the available data. Simply put, it is the degree to which a Data organisation can quickly and easily provide Data driven value to a company.
Here the expression “Data organisation” is important. It encompasses the Data infrastructure and the processes as well as the people working on, with and for the Data within the company.
For example, a business manager can ask: “How long will it take the company to integrate these new Data sources and make the analyses that come with news business partners?”
How to measure your organisation’s agility level?
In order to answer the question above, here are dimensions against which Data managers can estimate how the organisation is faring.
- Quickness of Decision-Making and Action:
Data agility enables organisations to make rapid decisions by providing access to relevant and up-to-date information. In dynamic business environments, delays in decision-making can lead to missed opportunities or increased risks.
Question to assess this dimension: “What is the typical cycle time for a new analysis?” or “Do business managers get the information they need in time?”
- Adaptability to Change:
Data-agile organisations can easily adapt to changes in market conditions, customer preferences, or internal processes. The ability to quickly analyze and interpret data allows for agile responses to emerging trends or shifts in the business landscape.
Question to assess this dimension: “Can the Data organisation provide the same level of quality and freshness of data analyses if we move to a new market or integrate a new business model?”
- Data Integration:
Data agility involves the seamless integration of data from various sources. This integration enables a comprehensive view of information, facilitating better-informed decisions. As businesses grow, the number of data sources they must deal with is expected to grow. This integration does not solely lie on the shoulders of Data engineers.
Question to assess this dimension: “How easy is it for data organisation to incorporate a new data source?” or “How long does it take to connect with a new business partner?”
- Data Infrastructure Scalability and Responsiveness:
Scalable data infrastructure and analytics processes are essential for data agility. As the volume of data grows, organisations need systems that can handle the increased load while maintaining responsiveness. It should be designed to respond rapidly to changing data needs. This includes flexible data architectures, real-time processing capabilities, and the ability to scale resources as required.
Question to assess this dimension: “Can our Data infrastructure withstand an uptick in data activity such as the rapid increase in the amount of data to store and analyse?” or “Can the infrastructure allow for more complex and numerous data analysis?”
- Collaboration and Accessibility:
Data agility promotes collaboration by ensuring that relevant data is accessible to different teams and departments within an organisation. This accessibility fosters cross-functional collaboration and a shared understanding of data-driven insights.
Question to assess this dimension: “How siloed is the data organisation?” or “To what extent department A (within the company) is aware of the data available in department B?”
- Iterative and Continuous Improvement:
An agile data culture involves an iterative approach to data analysis and decision-making. Continuous improvement is achieved by regularly assessing the effectiveness of data-driven strategies and making adjustments based on feedback and evolving requirements.
Question to assess this dimension: “Are there rituals in place to exchange feedbacks and track related changes within the data organisation?”
- Users Empowerment:
Data agility empowers a broader range of users within an organisation to interact with and derive insights from data. This often involves user-friendly analytics tools and platforms that do not require advanced technical skills. This is the paramount level of not only data agility but also data maturity.
Question to assess this dimension: “To what degree the business team needs the help from the data team to create new analysis?”
A highly Data agile organisation is sure to easily and rapidly create knowledge from its data thus uncovering valuable insights about its products, market and competition.
Now that we understand the usefulness of this concept, we should be asking yourself this question: “How Data-agile is my organisation?”
How to improve on your (organisation’s) Data agility?
In order to improve on your Data agility, many dimensions can be considered.
Company culture and organisation:
First and foremost, the company culture must be aligned with its strategic ambitions. That culture should not get in the way of activities related to Data. A good indicator of issues with the company culture is the presence of siloes.
- How easy is it for one person on team A to talk to someone on team B?
Another factor to consider is the culture of openness within the company. A team in which open expression is discouraged is bound to underperform.
- Are employees from different divisions aware of what projects others divisions are working on at the moment?
Data infrastructure:
Companies in majority have migrated their infrastructure on the cloud. In doing so, they get more flexibility to scale their activities, more security and a reliable platform. Systems become so complex overtime (e.g., on average, companies get data from up to 400 sources) that it becomes difficult and costly for companies to evolve their data value chain (i.e., integrate new data sources). Businesses with legacy systems face more those challenges to a higher degree.
- Is our data architecture up-to-date? What will be the cost and time we need to integrate new sources of Data or create new analysis?
Shortage of skillset or occupation on low-value tasks:
Some companies also face issues managing with their talent. On the on hand, they report facing a lack adequate talent to realise their data related ambitions. On the other hand, some employees report being tasked with activities that are not really on the path of value creation through data (e.g., the infamous data preparation and cleaning tasks).
- What is the ROI of the tasks our data spend the most time on?
Agility:
Finally, agility. It is not only about being able to risk-manage on projects but also about continuous improvement. Agile promote continuous exploration and learning by undertaking complex tasks one step at a time. Even in their data related activities, organisations should take a step back and reflect on their ways of working and making their teams interact.
- What have we learned from the past data product delivery? Do we take enough time during our current data projects to introspect and improve?
Agility also means for the data team to be able to work closely with users. An organisation that prioritises user feedback and involvement is sure to see quick and long lasting benefits (i.e., data-driven value) from its projects and everyday activities.
- How often do we take users feedbacks into account in our projects?
