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The data economy is growing at neck-breaking speed. Companies that aren’t collating, analyzing, sharing and monetizing their information risk losing their way or falling behind.

For the past decade, companies have been amassing data in order to better satisfy their customers, streamline their operations and improve their strategy. But while they know that data is potent, many still aren’t using this superpower to the max. 

As technology advances, workflows become smarter and interactions between humans and machines become more seamless, companies will soon be forced to wring data out of every area of their business in order to keep up. It is innovation based on fact, or growth based on truth.

Some organizations are storming ahead in this data-driven era, turning to the information held in their company coffers before making decisions. Others aren’t quite sure how to capture it, let alone interpret it.

McKinsey estimates that by 2025, employees will be using data to optimize nearly every aspect of their work. This isn’t far away, and that’s the point, the consulting firm says.

Its January 2022 report, ‘The data-driven enterprise of 2025’, aims to help executives understand and tap into the value of data, with resources and tips on how to get started.

It claims that companies wanting to glean the highest value from their data must exhibit seven characteristics. While some organizations are already demonstrating some of them, others are just beginning the journey.

These seven characteristics are:

  1. Embedding data into business workflows
  2. Processing and delivering data in real time
  3. Using flexible data stores to integrate data 
  4. Acquiring a data operating model to process data
  5. Expanding the role of data officers
  6. Sharing data with other organizations
  7. Prioritizing and automating data management

A Data Mindset

The first way to make a change, as is often the case, comes down to altering the mindset.

Evidence has shown time and time again that the biggest obstacles to creating data-driven businesses aren’t technical but cultural. It’s one thing to consider data in a process, quite another to make this habitual.

Many organizations still rely on traditional strategies to manage day-to-day activities, and worn-out road maps to solve problems, which can take months or years.

Companies must show employees how data-driven strategies can automate daily tasks and turn scrappy maps into high-tech global positioning systems that enable them to resolve challenges faster, while giving them absolute confidence that they’re headed in the right direction. 

Translated into the day-to-day, this could be store managers applying real-time analytics to identify and direct loyalty program customers to other products they may like as they shop. Or it could be telco network operators identifying areas that require maintenance, or places in the network with opportunities to expand based on usage. 

It could also be procurement managers applying data-driven processes to triage purchases for approval, for example.

Data-driven processes not only increase productivity, but also free up staff to focus on more human skills such as innovation, collaboration and communication, which is motivating for them and beneficial for your business.

Good Tech Combined with Passionate Staff

However, data only becomes powerful when there is the right technology to collate, process, query and analyze it.

As Peter Sondergaard, Senior Vice President of Gartner Research, famously said, “Information is the oil of the 21st century, and analytics is the combustion engine.”

To get that engine whirring, organizations need a cloud-based infrastructure as well as architecture that supports real-time analytics, plus flexible databases and data-modeling tools that allow them to query the data.

Imagine having the capability to integrate customer data from multiple sources into a single, 360-degree view of each person that can be modeled in real time, or creating a digital twin of the supply chain that enables application of what-if scenarios.

By 2025, every business should have at its core Chief Data Officers and teams dedicated to data, according to McKinsey.

The cost may be less than most companies anticipate. As the price of cloud computing continues to decline and more powerful data tools become available, sophisticated data analytics are within reach for all organizations. 

And with the right tech in place, it just takes people to enhance its capability. By 2025, every business should have at its core Chief Data Officers (CDOs) and teams dedicated to data, according to McKinsey.

These teams will not only be responsible for ensuring the quality and security of the data, but also for creating new ways to use it.

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This could be delivering new subscription-based services for clients that target their specific likes or needs, or working with a sales team to use data to drive sales conversions.

Companies can be Stronger Together

CDOs should also be seeking ways to monetize data through services and sharing. Data-sharing arrangements with external partners and competitors are certainly increasing, though they remain uncommon, with most data siloed within organizations. 

The data economy is growing fast, according to McKinsey, and as the exchange of data grows, barriers will come down. 

Within a few short years, organizations will be using data-sharing platforms to facilitate collaboration and gain even wider insights.

For example, data sharing will enable manufacturers to create open platforms that reveal a broader picture of global supply chains, or pharmaceuticals and healthcare providers to pool data on patients or clinical trials for their mutual benefit.

The data economy is growing fast, according to McKinsey, and as the exchange of data grows, barriers will come down.

But wider use of data must be accompanied by increased awareness around consumer rights and security, given the increasingly high stakes of hacks.

Thankfully, automated, near-constant backup procedures ensure data resiliency, and the ability to recover the ‘last good copy’ of data in minutes rather than days or weeks.

At the same time, AI tools are able to ensure data quality by automating identification, correction and remediation.

Combined, these efforts enable companies to have confidence in both their data and how it’s managed. This will ultimately help them to power their data-driven services into tomorrow and stay ahead of the game.

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