Insights

Businesses suffer from a bad case of TMI (too much information)

Andy Morin, Chief Solution Architect – UST Xpanxion

In this digital age, when data is at the heart of digital transformation and expected to drive every business decision imaginable, the proliferation of data and the number of data sources are surging at a staggering rate and becoming tougher to manage and master.

A study by Forrester asserts this and reveals three particularly telling data paradoxes hindering the path toward digital transformation today.

• 67% of organizations desire more data than they can currently manage, while 70% claim to gather data faster than they can analyze or use it.
  The result: While businesses covet more data, they are overwhelmed with the data they currently have, and they're wrestling with how to make that data valuable.
• More than six in 10 businesses think an as-a-service model would help them become more agile and scalable and release applications quickly and without issues. Yet, only 20% of companies have shifted most of their applications and infrastructure to modern cloud, edge computing, or other distributed as-a-service models.
  
  The result: Even though businesses see tremendous value in modern architecture, the vast majority of companies still need to make a complete migration to modernization. These companies hold on to difficult-to-use legacy systems, databases, software, and applications, time-consuming manual processes, and hard-to-access data sources. These legacy systems are holding companies back, sucking precious resources and time and delaying or prohibiting digital transformation and the innovation needed to survive in the digital world.
• Two-thirds of respondents claim to be data-driven businesses and view data as “the lifeblood of their organization.” But, only 21% treat data as capital and give it a companywide priority.
  
  The result: Businesses drastically overestimate their data readiness. Forrester created a data readiness scorecard based on an organization’s culture, data skills, and technical ability to collect and analyze data. The majority of respondents scored low in both technical proficiency and culture.

Hurdling the data overload roadblocks

No matter how you slice the numbers, they add up to the truth that most organizations are unable to realize time to value. Whether the roadblocks are accessing the data, transforming it into analytical formats, analyzing it, or getting it to the right place at the right time, organizations are constantly grappling with how to decrease the time it takes to turn their data into value.

These are ironic problems to have in this decade of data. The ramifications of data overload are extensive and profound. Businesses must extract value from data to have a clear path to digital transformation. Consequently, they have no way to achieve data sovereignty.

Forrester points to three main obstacles that contribute to data overload:

• Inadequate in-house data scientists and technical talents
• Business and data silos (six in 10 businesses contend with silos that render the data hard to access)
• Sluggish and arduous manual processes.

In other words, for businesses to effectively tap value from data and achieve data excellence, they must make the following moves with the right combination of technologies, culture, and teams:

• Invest in a data-ready skillset and culture. A precise set of skills is required to glean insights from data. Organizations should not cut corners here. They must discover and invest in the right data-ready talent and culture, whether in-house, through partners, or other third parties. Teams should be cross-functional and highly collaborative to achieve agility and adapt to rapid change.
• Cultivate talent beyond training and certifications in data literacy by inciting employees and teams to innovate in data and analytics and constantly evangelize the democratization of data companywide.
• Bridge the gaps between data, applications, and infrastructures. By bringing the infrastructure and its applications closer to the data, decision-making can occur at the right time, in near real-time, and when the data is at its freshest state. This move entails the adoption of modern IT infrastructures and multi-cloud environments so software and applications can run closer to where the data resides, is collected, analyzed, and acted upon (at the edge).
• Move to a data-as-a-service (DaaS) model. As the name implies, DaaS is a software service for data. It encompasses data management, storage, and analytics, allowing data to be shared across various clouds, systems, gateways, applications, etc., regardless of the data source location. Common application programming interfaces are used to access the data. DaaS is how businesses can quickly and efficiently break through data silos to create new value.
• Automate across the lifecycle. The sheer velocity, abundance, and diversity of today’s data require businesses to take advanced automation seriously. They should leverage machine learning and artificial intelligence to automate business and data processes, pipelines, and quality assurance testing so the data can flow effortlessly across its lifecycle.

Today, company success is increasingly measured by how well an organization can exploit data, apply analytics, and embrace new technologies, processes, and cultures. And although the value of most of today’s data remains untapped, there is a path toward data excellence…it's paved with the right blend of data-ready technologies, culture, and teams.

To learn how UST Xpanxion can help your business become data-ready, tap data’s full potential, and draw the business insights needed in today’s data economy, speak to our data experts today.