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Too Much of Anything Is Bad, Especially Enterprise Data

In 2006, Clive Humby, a British data science expert, said, "data is the new oil." The Economist reiterated this statement in its 2017 article titled, "The world's most valuable resource is no longer oil, but data." And sure enough, in 2021, the world will produce 74 zettabytes of data.

Data has become an essential weapon for companies everywhere in the modern digital economy. Data drives and informs every aspect of enterprise strategy — from customer relationship management, marketing, and sales to finance, innovation, engineering, and logistics.

Organizations need data to improve processes, understand their customers, and make better decisions to improve profitability, ROI, and competitiveness.

But can there be such a thing as too much data?

  • The Biggest Data Challenges for Enterprises

With modern analytics practices, companies can now unlock massive potential for business growth and success. They can forecast market trends, accelerate innovation, reduce time-to-market, reach new demographics, and streamline operations.

In fact, with the correct quantity, quality, and type of data, organizations can now achieve goals that were previously considered difficult or even impracticable. And yet, data is not without its challenges.

Here are four key challenges:

  • It's Difficult to Manage Large Data Volumes

Today's organizations must contend with large volumes of data generated by disparate sources and housed in disparate systems. While this data can be leveraged for numerous applications, many businesses struggle to manage, investigate, and consolidate it into a unified data architecture.

  • Poor Data Quality Generates Poor Results

Poor quality data generates poor results that have no value for the enterprise or its stakeholders. This is a pervasive problem when analytics teams try to pull in more and different data types from various sources that utilize unique data collection methods and formats. In such a scenario, duplicate entries, typos, contradictions, outdated information, and inconsistencies become more common as the data sources and volumes multiply. This impacts the data's quality, reliability, and usability.

  • Data Security and Integrity Can Be Compromised

The integrity of the organization's data considerably depends on how it is collected, processed, stored, secured, and updated. Disparate data in sub-optimal silos prevent the creation of "one version of the truth," which adversely impacts data analyses and decision-making.

Security is another big challenge that affects data integrity. As the number of data sources and interconnecting nodes increases, security vulnerabilities are introduced into the data system. As the touchpoints increase so does the surface area available to malicious elements to probe, increasing the probability of cyber-attacks and data breaches. Data loss can be catastrophic, considering that a single such incident can cost $4.24 million on average.

  • High Data Handling Costs and Limited Scalability

As organizations expand their access to richer datasets, more computing and human resources are required, which increases data handling costs. This is true of both data platforms in the cloud and on-premise.

Cloud platforms elastically scale to meet demand, which can drive up costs, while on-premises data solutions can be expensive due to the costs of new hardware and software, power, developers, administrators, etc.

Furthermore, scaling big data systems to accommodate data growth changes can also add to costs and increase the organization's technical debt.

  • Addressing Data Challenges with Data Management and Data Governance

Data is a valuable resource that plays a crucial role in organizational growth and success. But to realize those benefits, companies must find ways to extract value from it.

It's noteworthy that for the best results, raw data must be pre-processed to remove inconsistencies, inaccuracies, and duplicate entries. But there's more to it.

Holistically, it's the application of effective Data Management and Data Governance practices that solve the outlined data challenges. These include:

1. Ensuring the integrity of the data that underpins business decisions and operations

Enabling stakeholders across departments and business lines to collaborate on one version of the truth through unified, integrated big data solutions helps support self-service analytics for faster insights and problem resolution.

2. Getting the most out of the data

Businesses must first ensure that they have both high-quality and timely data. With robust governance processes in place, data rigor can be ensured through necessary data cleansing, transformation, and enriching activities. This guarantees that all stakeholders can derive maximum value from their data.

3. Optimizing the accuracy of predictions

Building on the foundation of sound governance practices, analytics solutions can help predict outcomes to help managers make intelligent decisions based on facts, not assumptions or hunches.

4. Improving data security and preventing data breaches

By now, we have understood that data security is a critical concern in the digital economy. Ensuring that information flows in one direction, with data flowing into the analytic hub and not rushing back out to production applications, increases security.

5. Reducing time cycles for harnessing insights from data

The data lake solution enables users to quickly query and interact with disparate data sets otherwise tricky or impossible to access. As a result, businesses can swiftly gain insights from data, which leads to higher productivity.

6. Instilling data governance in the very fabric

Data Governance processes are more effective when they are established early in the project lifecycle and applied consistently across all the succeeding stages of data operations. This helps ensure that essential data operations are performed thoroughly, thereby reducing errors and improving overall quality.

Of course, all the above facets are achievable by streamlining the data lifecycle; however, much emphasis should be laid upon the analytic methods, tools, and technologies in place. This must link with an effort in thinking strategically and designing a comprehensive approach that covers all functions and aspects. This is essential because once the enterprise becomes data-driven, the impact will be felt across the entire enterprise.

Suppose the enterprise is dealing with large volumes of unstructured data. In that case, it's important to use data analytics practices that are specifically designed to generate valuable and meaningful insights from disorderly data.

Besides, self-service analysis solutions based on ML algorithms, AI, and automation can help extract meaningful insights from data with minimal manual coding. Such tools are beneficial if the enterprise struggles with a dearth of data scientists and other data experts.

  • In a Nutshell

Data is the most valuable resource for modern organizations. However, its value can be severely limited if it is not properly managed or governed.

To sum up, data is not valuable if:

  1. Its quality is poor
  2. It comes from disparate sources that don't "talk" to each other"
  3. It doesn't provide the right context, and
  4. It doesn't generate actionable insights.

To convert raw data into useful intelligence, organizations must be more cognizant of common data challenges. More importantly, they must implement the right strategies and tools to address these challenges and make the best possible use of this "new oil." Connect with us to know how the organizations that have overcome these challenges managed to do so, and the gains that accrued to them after that.




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