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Disorganized Data Can Hold You Back — Here's How to Fix It

August 19, 2022 Digital Workplace
Siobhan Fagan
By Siobhan Fagan

Data management plays a crucial role in maintaining compliance with rules and regulations and keeping data safe. But it is also a key factor in companies' ability to meet their business goals.

The efficient use of data speaks to the core operational concept of capacity utilization. Enterprises that use their data more effectively will outperform their competitors, Garima Kapoor, COO and co-founder of Redwood City, Calif.-based Minio, said.

Kapoor backs her statement with research by Collibra which shows that data-driven companies are 58% more likely to beat revenue goals than non-data-driven companies — and 162% more likely to significantly outperform laggards.

Connecting the (Data) Dots

Efficient data use means connecting the right resources (both applications and teams) with the right data, wherever those may exist. “The last part is increasingly important,” Kapoor said. “Data is generated everywhere in the modern enterprise and often needs to be consumed and analyzed where it is generated rather than moving it to a centralized location.”

To solve the challenge of distributed data and data utilization, enterprises need to adopt data architectures that enable consistency across public, private and edge instances.

First, the software stack must support consistent identity management and policy-based access controls to ensure the right data is shared with the right teams and applications securely.

Along these lines, it is also important to remove data when it is no longer accurate or timely. This means adopting solutions that deliver consistent lifecycle management tools to allow administrators to define how long data remains on drives before being removed.

The data store must also be able to consistently protect data within and across clouds with a wide range of policies built on object and tag filters to declare expiry rules. This includes object locking and legal holds so enterprises can remain confident that data has not been modified.

Finally, considerations must be made for data to eventually be moved. The ability to persist and analyze that data in situ will increase efficiency and utilization, but at some point, the insights and associated data need to be centralized in compliance with existing regulations. This requires the ability to programmatically tier objects across storage mediums and cloud types to optimize for performance and cost.

“With the right architecture, enterprises can get better access to their data, more securely and with higher utilization,” Kapoor said. “This creates a virtuous cycle of insight, customer acquisition/retention and competitive advantage. That makes it a first-order business strategy."

Related Article: How Master Data Management Can Help Tame the Data Governance Mayhem

Data Is King

According to Kris Moniz, national data and analytics practice lead for Dayton, Ohio-based Centric Consulting, modern businesses cannot have an effective business strategy without an effective data strategy.

“Everything we do today generates data. Have a smart watch? You are generating data. Smart Phone? Data. Activity Monitor? Data. Buy something online? Data Data Data," he said. "As we all know, more of anything creates more complexity. The more data you gather, purchase or otherwise have access to as a company, the more crucial your data strategy is.”

But data by itself is meaningless. It takes proper governance, analytic enablement and a lot of hard work to turn raw data into valuable information that can help drive the right decisions to grow a business or to help pivot in volatile times.

Without a solid alignment of the data strategy and the business strategy, companies risk spending precious resources refining their data in ways that generate no demonstrable business value. The sunk cost and opportunity cost compounds over time as the company falls behind, and the competition gets further ahead.

Today's data tools are light years ahead of where they were at the turn of the century, both in terms of capability and cost. “The only barrier to entry left for most organizations is the political will to finally dive into this new world of data-driven decision-making and turn their business strategy, into an actionable and measurable plan to drive their organization into the future,” Moniz said.

Related Article: Why You Need Data Observability in the Digital Workplace

Reconciling Sources

One of the main challenges with data today is the fact that organizations must manage the influx of information from multiple sources — often because of the technical debt of legacy on-premises systems that manage different lines of business. Many companies also have data repositories that carry a range of difficult-to-reconcile sources. 

While moving to the cloud may solve some of those problems and help companies be more agile, this can also create data silos in a different guise, said Jamie Peers, VP of business development at Newark,Del.-based Synatic.  

Immaturity in data governance often means that key terms may not be defined consistently, creating another bottleneck for consolidation. Yet, the benefits of consolidating siloed data are clear for analytics and operational activities. These include:

  • More responsive products.
  • More sophisticated underwriting models.
  • An enhanced ability to manage risk.
  • Deeper and more reliable analytics.
  • Better fraud detection and compliance.
  • More effective management.
  • The ability to make real-time predictions.

“Trying to manage data transfer and consolidation involving data from different technologies, in batch or real-time streaming, with several data integration tools, makes it difficult for organizations to be agile and to quickly incorporate, integrate, analyze and share their data,” Peers said.

In order to move to a world where data is kept, consolidated and then used, new technologies and approaches are required. IT departments should aim to reduce the amount of time they spend storing, analyzing and presenting information to users, by establishing practices and implementing a data automation solution that promotes data access and analysis.

Related Article: Data Mesh or Data Fabric as a Foundation for Data Management Strategy

Top Down vs. Bottom Up

Data management plays out as a classic conflict between centralization and freedom across organizations. The top-down advocates want data secure, controlled, standardized and governed. The bottom-up advocates say that bureaucracy interferes with innovation.

According to Patrick Kopins, COO of IT service management company Accscient, companies can have the best of both worlds by keeping analytics at the forefront of their data management initiatives because, after all, data is only as beneficial as the insights an organization can glean from it.

Organizations have two basic workflow pipelines that drive data analytics:

  • Data operations: Raw data flows in from suppliers, undergoes cleaning and transformation, and feeds into analytics in the form of model results, charts and graphs.
  • Analytics development: Data analysts, scientists and engineers create, develop, test and publish new or updated analytics to data operations.

Think of these pipelines as a factory. Raw materials enter and pass through a series of steps and produce finished goods. Many organizations, Kopins said, operate their data factory like a 1940's automobile plant, with specially trained artisans manually performing operations — and quality being checked at the end of the line.

However, the best way for organizations to modernize their data management is to take a page from the modern manufacturing operations of the 2020's and beyond, Kopins said. Automate as much as possible. Check quality at every step in the process. Monitor operations in real-time. Minimize cycle-time and reduce errors to virtually zero.

“One thing to keep in mind: Data organizations often make the mistake of thinking that one tool is going to unify their data pipelines and simplify their data management activities," he said. "Instead, embrace the heterogeneous nature of modern data organizations."

In other words, companies should enable everyone to choose the toolchain and data center that best fits their requirements and consolidate the teams and data centers into a coherent whole using a solid analytics platform that performs meta-orchestration and provides a common test environment that spans all the pipelines.

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