Knowledge

Data Agility: Why do you need it?

What would happen if you were stranded at sea? The first thing that would affect you is dehydration because seawater is undrinkable. All that water and you cannot even take a sip. A similar situation is prevalent with big data and enterprises. There’s a lot of data available in today’s environment, but very little of it is usable and actionable. The focus so far has been on ensuring data protection for business-critical data rather than how to make sense of this data. Hence, modern-day enterprises are observing a pressing need for data agility. 

What is Data Agility?

Data agility is the speed and flexibility to satisfy the data demands of a business quickly, reliably, and at scale, regardless of underlying data infrastructures (e.g. hybrid cloud, multi-cloud).

Agility is a core component of overall data health — that is, how well an organization’s data supports its business objectives. Data can be considered healthy when it is easily discoverable, understandable, and of value to the people who need to use it, and these characteristics are sustained throughout the data lifecycle.

To support true data agility, an organization must have a flexible, scalable ecosystem with end-to-end data management. Only then will they be able to meet the changing demands of the business?

data agility

Why do you need it?

To create synergy between your enterprises’ IT and business.

Right now, there’s a massive disparity between the ability to collect/process the data and using the data to create value. We’re gathering data faster than the business can properly utilize it.

We’re living in the age of business intelligence backed by irrefutable data. Data agility management is the key to optimum business intelligence.

Most legacy systems allow you to store and process data well, but when it’s time for the business analyst to use this data, there is a dependence on the IT team to extract the required data. This dependence can be a big problem since the analyst often needs to look at some of the data and refine the requirement to narrow or expand the scope. The inability of the analyst to have direct access to the data impacts the organization’s ability to respond quickly to market inputs.

Data agility allows analysts to do their work without needing them to be IT experts to extract and work with the data. Analysts can get down to brass tacks without the pressure of balancing business needs with IT actions. Analysts can quickly sift through data to get insightful, actionable information without the hindrances of complex legacy systems. This ability will help your enterprise use data for better business intelligence.

Who needs Data Agility services?

Data agility services can be beneficial for any company that relies on data. This includes companies of all sizes in a variety of industries.

You can benefit from data agility services if your company collects, stores, or processes data.

Some of the most common benefits of data agility services include:

  • The ability to quickly adapt to changes in data
  • Improved decision-making about data
  • Faster processing of data
  • Reduced costs associated with data storage and processing

If your company wants to improve its data supply chain, consider investing in data agility services. These services can help you overcome some of the most common challenges of managing large amounts of data.

How can you improve Data Agility?

The truth is, that achieving true data agility is hard to do well. It’s not enough to invest in the right data stack for your brand’s unique needs (though, yes, you need to do that) – you also have to put in place a data culture where everyone who touches your data strategy is encouraged and given the resources they need to access the information that supports their work.

A good first step is to establish a mentality of data democratization across your company. Once you have that, it’s possible to go to work identifying and removing information silos with the necessary data infrastructure and strategy.

But without that first step, you’ll have shiny new tools, but you won’t have the company alignment you need to use them to their full potential. That leaves you at risk of falling into data disparity and fragmentation – or seeing your data team so overwhelmed with requests that they end up being reactive instead of focusing on more forward-looking initiatives. Either way, it’s a bad scene and not something you can build a great, sustainable customer engagement program on.

data agility

Conclusion

A more traditional approach to handling data is to load everything to a data warehouse and try to optimize throughput and how you gather insights. But the result of that choice is that you have to wait for data. Another way is to enable greater data governance with data stewards within the organization. This ensures the right people have the correct level of data access, and it also brings more efficient and agile decision-making.

One of the most interesting applications of data agility is connecting multi-party data ecosystems. Businesses looking to expand into data-driven business models and revenue streams are starting to use data and collaborate with third parties outside of their organizations. They aggregate data from multiple parties and multiple providers and take their data supply chain to a whole new level.

Data agility is not a new concept, but it is often misunderstood, especially where it spans beyond the borders of an organization. When done correctly, implementing data agility into your data supply chain can help to improve efficiency and quality while also reducing costs.

Knowledge

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