Overcome the Challenges of Data Analytics Implementation
Data analytics can transform an organization, and the right solutions make it easier to execute.
Data-driven decision-making requires users to understand the value of data and learn how to incorporate it into processes. Data can become a crucial business driver and help organizations innovate logically.
Data can also help organizations improve the customer experience, support internal teams, inform employee training programs and enhance a company’s brand. Achieving a data-driven approach often requires a full view of an organization’s data and consistent security.
When implementing a data-driven approach, an organization must prepare for technical change — for example, establishing governance and maintaining security. However, with the right solutions, these challenges can easily be resolved.
53%
The percentage of respondents who feel access to data is more critical or far more critical since the onset of the COVID-19 pandemic
Source: Enterprise Management Associates, “The 2021 State of Data and What’s Next,” February 2021
Enabling Effective Data Analytics
For business and IT leaders whose organizations have not yet employed data analytics in their operations, the subject can feel daunting. Indeed, as with any new technology, analytics presents potential challenges — not only with the implementation and management of analytics platforms but also with security, governance and even organizational culture.
Tailoring solutions to data discovery, storage, analysis and other functions helps ease organizations’ path to effective data analytics and sets up teams for success. Many organizations look to the public cloud for data analytics capabilities, gaining increased flexibility.
Challenges
Data cataloging: A data catalog is an inventory of data assets within an organization, which can help data scientists and other stakeholders to collect, organize, access and enrich metadata that assists with overall data management. Without a data catalog, many business and IT leaders would struggle to identify the sources of data within their organization, let alone where all that data resides.
Security: Data security should be a key component of any IT initiative, especially one centered on Big Data (where a breach could potentially expose massive amounts of information to cybercriminals). In fact, some observers, including Forrester Research, have dubbed highly sensitive information — such as health information, credit card data, bank account details and Social Security numbers — as “toxic data” due to its ability to harm an organization if it falls into the wrong hands.
Governance: Data governance encompasses the policies, processes and organizational structures that support data management and analytics. Organizations face many governance challenges, such as where and how long to store data, who can access which data (and how they can access it), how data is shared across the organization and how to cull redundant records that contain identical information.
Culture: As they adopt new data analytics tools and processes, organizations must shift their cultures to embrace both employee experiences and data-driven insights. For instance, executives and IT leaders should be careful not to discount the experience of sales team members, who have intimate knowledge of their customers’ needs, and instead enhance that firsthand knowledge with insights derived from data analytics tools.
Expertise: Often, organizations enter their data analytics efforts with a specific, narrow use case in mind. That’s great for getting started, but if they lack internal analytics expertise, they may overlook opportunities to expand into new applications. Organizations should seek to maximize their access to analytics talent and should encourage their data professionals to look for new ways to derive value from data.
Solutions
Five key capabilities are necessary for data analytics. Numerous tools can provide some of these capabilities, and organizations must choose the right mix of solutions to help them achieve their desired outcomes.
Capability 1 — Store: Advanced storage solutions provide a solid foundation for data analytics initiatives. These include next-generation databases, data warehouses and data lakes. Next-generation databases are specifically built for speed and scale, while a data lake is a large pool of raw data. A data warehouse, by contrast, is a repository for structured, filtered data that has already been processed for a specific purpose.
Capability 2 — Transform: The next step on the data analytics journey is for organizations to transform their stored data into a more easily accessible asset. For instance, data cleansing solutions standardize data formats, and data merge solutions combine data from multiple sources. Master data management tools allow business and IT leaders to create a “single source of truth” across their organizations. Without MDM tools in place, many organizations struggle to answer relatively basic questions, such as who their most profitable customers are, or which products have the highest profit margins.
Capability 3 — Discover: Data discovery tools, such as data catalogs, help organizations to arrive at a better and more complete understanding of what data assets exist in their environments. Only after engaging in data discovery can business and IT leaders begin to make an informed assessment about what objectives can be achieved using the information resources they already have.
Capability 4 — Analyze: To power data analysis, organizations must invest in solutions such as data visualization tools and IT operational analytics. ITOA can assist with functions such as root cause analysis and is frequently an early use case for organizations investing in data analytics for the first time.
Capability 5 — Operationalize: Operationalizing data analytics simply means making data and analytics a regular part of an organization’s operations. This involves tying together previously mentioned tools and using governance and a strategic vision to continue maximizing the value of an organization’s data.
Story by Aaron Colwell, the leader and founder of CDW’s Data Platforms and Insight team. He has worked for over 15 years in the IT industry helping customers solve complex problems and reach their desired technical and business outcomes with data platforms, analytics and security solutions.
23%
The percentage of IT professionals who chose object storage because it simplified data governance
Source: Enterprise Management Associates, “The 2021 State of Data and What’s Next,” February 2021
To learn how data analytics solutions can transform your business approach, read the white paper “Achieving Effective Data Analytics” from CDW.
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