Data agility has emerged as one of the hottest business topics today, with people weighing in on its importance, to everyone from the CIOto the sales team. And what is data agility, exactly? I think one of the best ways to define data agility is to think of it the same way you think of human physical agility. It’s about being able to move and change quickly in order to adapt to what’s going on in your environment. To achieve this ability, you need to maintain a level of fitness that makes fast responses and sudden shifts possible. I believe that’s how it works with data, too.
To achieve data agility, you have to take steps to ensure that your data is in top shape, to support using it in real time to quickly adjust marketing and other business strategies to meet changing customer needs. And just as you can take specific steps to improve your physical fitness and agility, so too can you take steps to improve your data agility. Here’s what I suggest.
Assess your data’s condition
Achieving physical agility can be a challenge for someone who’s never been particularly active, and that’s true of data, too. The first step in undertaking any kind of fitness program is to evaluate your condition to establish a baseline. So how’s your data doing now? Is it sluggish and slow, or quickly available for you to use for decision making? What shape is it in? Is it a mess of duplications? Not updated nearly often enough? Or is it clean and fit and ready to apply to strategic activities? Take an honest look and see where your data could use a workout.
Establish a culture of agility
Agile data starts with an agile mindset. The days of collecting historical data, analyzing it, applying it and then waiting weeks, months or more to see what happens are over. Encourage teams across your organization, from research and development to sales and marketing, to think about the business advantages to be gained from being able to think and move quickly, in real time, in response to changes in customer behavior or market conditions. That’s the agile culture businesses that excel today constantly strive for, and agile data is part of it.
Leverage all your data sources
You’ll never achieve maximum physical agility doing just one activity; you have to draw from all the resources available to you — aerobics, strength training, stretching, etc. The same principle applies to data agility. Start by documenting all the customer data your business produces, from all sources (both internal and external), and by analyzing what types of data you’re collecting (and not collecting). Then look at deploying a customer data platform that will unify all the data from multiple sources to make it easily accessible centrally and in real time so you can analyze and apply it to business decisions.
Think in real time
Data agility is about being able to respond to what’s happening when it’s happening, to “seize the moment,” if you will, so that your actions will have maximum impact. That means you have to let go of the old data warehouse model, where you make decisions about structuring and cleaning data over time, based on pre-defined questions and expectations. As the news portal Datanami puts it, “Agile organizations do not have the luxury to take weeks or months to gather and normalize data into a data warehouse, as was traditionally done.” Rather, you have to be able to make data decisions on a just-in-time basis — and to rethink them quickly whenever you need to.
Keep your data clean
I firmly believe that agile data is clean data. With massive amounts of information coming in from multiple sources — now including wearables, sensors and IoT devices — and data being stored on multiple platforms, it’s easy to get weighed down by problems like data duplication, inaccuracy and incompleteness. You have to eliminate these impediments to agility; as a starting point, I recommend checking out the tips in a recent iMedia Connection article on how to not just capture data but also keep it as clean and useful as possible.
Get prepped to make your data usable
I’ve advocated for the role of artificial intelligence (AI) in making big data more usable and applicable in business in a previous article, and I believe that data agility is very much a key to data usability. AI adds an intelligence layer to big data, making it possible to conduct data analytics faster and on a much larger scale than teams of data analysts could ever hope to do. It’s definitely something worth thinking about as you look for ways for data to respond nimbly to customer and market shifts.
There’s a culture and a way of thinking that gives rise to all the physical fitness programs so many companies encourage employees to participate in today. That’s true of data agility, too. The first step is to recognize its value to your organization. Then you can move on to all the specifics of how you’re going to achieve it. Data agility is as much about an organizational attitude as it is about the tools and technology that help achieve it. I hope this list has given you a good start to making your data more fit.
To view the orginal post, please visit Forbes.com.