01
Achieve Order
As Data Explodes
Today, companies store and manage a nearly incalculable amount of data. Given the surging size and scale of information, many companies struggle to harvest actionable insights. Ishit Vachhrajani, enterprise strategist at Amazon Web Services (AWS), says companies face two competing priorities when it comes to addressing data complexity.
“I think the challenge stems from this perceived tension between the desire to treat data as an asset for the whole company by enabling access to that data at scale, while maintaining controls that satisfy governance standards,” he says. As its size and variety multiplies, data becomes more difficult to control and use.
To effectively tackle data sprawl, use data management tools to organize information, maintain its quality and translate insights into action. This starts with implementing a robust cloud infrastructure that consolidates data into a “data lake”—a centralized repository that houses data in its rawest form, whether structured or unstructured, for easier access at any scale.
02
Break Down
Data Silos
Data typically sits in silos within individual departments, with each business unit relying on its own operational data to achieve its goals. In fact, 80% of companies report having either a moderate or high degree of data siloing. This results in wasting valuable resources: On average, companies don’t use 97% of their enterprise data.
“In many organizations, you see that data is jealously guarded by specific functions or teams, and therefore it becomes hard to access for others who could benefit from that data,” says Luc Hennekens, an enterprise strategist at AWS.
It’s critical that companies resolve fragmentation to unlock business insights and innovation.
Data lakes are again an essential solution. With data warehouses or traditional approaches to data management, organizations must decide beforehand how they’ll utilize data and preprocess it for specific team functions. Data lakes, meanwhile, are centralized, allowing different teams across the organization to deploy the same raw datasets according to their business needs.
‘It comes down to having agility and flexibility in how you’re using data, and an ability to correlate and comingle different datasets,” says Vachhrajani. “That’s where complex business problems can be solved.”
03
Simplify
Your Legacy Infrastructure With Cloud
Breaking down silos and creating more organizational innovation, according to Vachhrajani, is complicated by legacy infrastructures. “The volume of data is growing, and the legacy way of maintaining, storing, processing and analyzing data simply cannot scale because of old-guard databases and because of the expensive nature of how data is managed in those systems and tools.”
Built to scale, the cloud can help companies conquer these pitfalls and drive modernization by fostering the data-driven experimentation required for new discoveries and inventive products.
“The cloud helps your company try more experiments and say yes to more ideas,” says Vachhrajani. “Even with massive amounts of data, you can convert these successful experiments into real, transformative products and launch them in a matter of minutes and days rather than months and years.”
04
Unify Teams
And Tools Across The Company
“Companies sometimes focus on the technology and underestimate the work that needs to be done on culture. This is one of the more complex things to achieve in any transformation,” Hennekens says. “If you look at how organizations have been operating for the last 40 years or so, we break everything up in these silos. We give people objectives by silo. We budget by silo. People start to identify with their own internal organization, and that’s their lens on the world.”
A first step? Enable data-driven decisions at all levels of the company. Help employees think holistically about data and the results it can drive for the entire business—not just their team.
“Even though technology can tremendously help and liberate the data that’s locked up in those silos, we have to change the mindset of people to think big picture first, organization first and customer first,” Hennekens says.
05
Make Data Security Everyone’s Job
Increasing data volume and complexity is transformative but also dangerous. Without full visibility into how their information is accessed, shared and utilized, organizations can’t properly mitigate security risks.
The answer is to ensure data security is on every employee’s radar. Build a security-driven culture where everyone has a sense of ownership for safeguarding data from threats.
“It's important to think about security as not something that only somebody on the cybersecurity or data privacy team is responsible for. Everyone is responsible and owns security,” Vachhrajani says. “We consider security as job zero for everyone.”
06
Bridge The Skills Gap
The final frontier of innovating with data is to employ artificial intelligence (AI) and machine learning, which can drive automation by processing and analyzing large amounts of data. People are a key part of this equation. Research shows that 82% of IT leaders believe AI will drive major changes to roles and skills within their organizations over the next three years.
“You absolutely need a plan to increase data proficiency within your organization,” Vachhrajani says.
Companies don’t necessarily need to hire an army of data scientists. Instead, they can lean on AI-enabled tools to help enrich their internal skills and capabilities.
This option to learn by doing—giving employees hands-on practice with AI-based training tools—is another inherent advantage of flexible, scalable cloud infrastructure, explains Vachhrajani. “The wonderful thing about the cloud is that it enables any developer with very limited or no machine learning experience to build AI and machine learning-infused products by democratizing access to these advanced capabilities.”
With this skills transformation as the final piece, companies will be well on their way to accelerating data transformation within their organizations.