10 Data Analytics Challenges and Solutions

Michael Chen | Content Strategist | June 27, 2024

Business leaders know using their data is important, but companies still struggle to effectively harness data to drive better decision-making and improved business results. After all, data sources tend to be optimized for data storage, not analytics. This makes it more difficult for businesspeople to digest. Meanwhile, businesses are wrestling with how best to apply technologies such as artificial intelligence, machine learning, and natural language processing—without hiring a squadron of data scientists. It’s a worthwhile effort because data analytics can help businesses identify patterns, trends, and opportunities that inform a wide range of strategic decisions, such as which products to invest in, which marketing campaigns to run, and which customers to target.

But without a formal strategy and targeted technology for collecting and analyzing relevant data, organizations risk making decisions based on intuition or assumptions, while missing out on opportunities to improve financial results and employee and customer experiences.

Top 10 Data Analytics Challenges for Businesses

Data on its own isn’t all that useful—it’s the analysis of data that lets teams make more informed decisions and respond better to changing business conditions. Data analytics as a process is central to an organization becoming truly data-driven. However, crafting, implementing, and running a data analytics strategy takes time and effort, and the process comes with some well-known yet formidable challenges.

1. Data quality

One of the biggest challenges most businesses face is ensuring that the data they collect is reliable. When data suffers from inaccuracy, incompleteness, inconsistencies, and duplication, that can lead to incorrect insights and poor decision-making. There are many tools available for data preparation, deduplication, and enhancement, and ideally some of this functionality is built into your analytics platform.

Non-standardized data can also be an issue—for example, when units, currencies, or date formats vary. Standardizing as much as possible, as early as possible, will minimize cleansing efforts and enable better analysis.

By implementing solutions such as data validation, data cleansing, and proper data governance, organizations can ensure their data is accurate, consistent, complete, accessible, and secure. This high-quality data can act as the fuel for effective data analysis and ultimately lead to better decision-making.

2. Data access

Companies often have data scattered across multiple systems and departments, and in structured, unstructured, and semi-structured formats. This makes it both difficult to consolidate and analyze and vulnerable to unauthorized use. Disorganized data poses challenges for analytics, machine learning, and artificial intelligence projects that work best with as much data as possible to draw from.

For many companies, the goal is democratization—granting data access across the entire organization regardless of department. To achieve this while also guarding against unauthorized access, companies should gather their data in a central repository, such as a data lake, or connect it directly to analytics applications using APIs and other integration tools. IT departments should strive to create streamlined data workflows with built-in automation and authentication to minimize data movement, reduce compatibility or format issues, and keep a handle on what users and systems have access to their information.

3. Bad visualizations

Transforming data into graphs or charts through data visualization efforts helps present complex information in a tangible, accurate way that makes it easier to understand. But using the wrong visualization method or including too much data can lead to misleading visualizations and incorrect conclusions. Input errors and oversimplified visualizations could also cause the resulting report to misrepresent what’s actually going on.

Effective data analytics systems support report generation, provide guidance on visualizations, and are intuitive enough for business users to operate. Otherwise, the burden of preparation and output falls on IT, and the quality and accuracy of visualizations can be questionable. To avoid this, organizations must make sure that the system they choose can handle structured, unstructured, and semi-structured data.

So how do you achieve effective data visualization? Start with the following three keys concepts:

Know your audience: Tailor your visualization to the interests of your viewers. Avoid technical jargon or complex charts and be selective about the data you include. A CEO wants very different information than a department head.

Start with a clear purpose: What story are you trying to tell with your data? What key message do you want viewers to take away? Once you know this, you can choose the most appropriate chart type. To that end, don’t just default to a pie or bar chart. There are many visualization options, each suited for different purposes. Line charts show trends over time, scatter plots reveal relationships between variables, and so on.

Keep it simple: Avoid cluttering your visualization with unnecessary elements. Use clear labels, concise titles, and a limited color palette for better readability. Avoid misleading scales, distorted elements, or chart types that might misrepresent the data.

4. Data privacy and security

Controlling access to data is a never-ending challenge that requires data classification as well as security technology.

At a high level, careful attention must be paid to who is allowed into critical operational systems to retrieve data, since any damage done here can bring a business to its knees. Similarly, businesses need to make sure that when users from different departments log into their dashboards, they see only the data that they should see. Businesses must establish strong access controls and ensure that their data storage and analytics systems are secure and compliant with data privacy regulations at every step of the data collection, analysis, and distribution process.

Before you can decide which roles should have access to various types or pools of data, you need to understand what that data is. That requires setting up a data classification system. To get started. consider the following steps:

See what you have: Identify the types of data your organization collects, stores, and processes, then label it based on sensitivity, potential consequences of a breach, and regulations it’s subject to, such as HIPAA or GDPR.

Develop a data classification matrix: Define a schema with different categories, such as public, confidential, and internal use only, and establish criteria for applying these classifications to data based on its sensitivity, legal requirements, and your company policies.

See who might want access: Outline roles and responsibilities for data classification, ownership, and access control. A finance department employee will have different access rights than a member of the HR team, for example.

Then, based on the classification policy, work with data owners to categorize your data. Once a scheme is in place, consider data classification tools that can automatically scan and categorize data based on your defined rules.

Finally, set up appropriate data security controls and train your employees on them, emphasizing the importance of proper data handling and access controls.

5. Talent shortage

Many companies can’t find the talent they need to turn their vast supplies of data into usable information. The demand for data analysts, data scientists, and other data-related roles has outpaced the supply of qualified professionals with the necessary skills to handle complex data analytics tasks. And there’s no signs of that demand leveling out, either. By 2026, the number of jobs requiring data science skills is projected to grow by nearly 28%, according to the US Bureau of Labor Statistics.

Fortunately, many analytics systems today offer advanced data analytics capabilities, such as built-in machine learning algorithms, that are accessible to business users without backgrounds in data science. Tools with automated data preparation and cleaning functionalities, in particular, can help data analysts get more done.

Companies can also upskill, identifying employees with strong analytical or technical backgrounds who might be interested in transitioning to data roles and offering paid training programs, online courses, or data bootcamps to equip them with the necessary skills.

6. Too many analytics systems and tools

It’s not uncommon that, once an organization embarks on a data analytics strategy, it ends up buying separate tools for each layer of the analytics process. Similarly, if departments act autonomously, they may wind up buying competing products with overlapping or counteractive capabilities; this can also be an issue when companies merge.

The result is a hodgepodge of technology, and if it’s deployed on-premises, then somewhere there’s a data center full of different software and licenses that must be managed. Altogether, this can lead to waste for the business and add unnecessary complexity to the architecture. To prevent this, IT leaders should create an organization-wide strategy for data tools, working with various department heads to understand their needs and requirements. Issuing a catalog that includes various cloud-based options can help get everyone on a standardized platform.

7. Cost

Data analytics requires investment in technology, staff, and infrastructure. But unless organizations are clear on the benefits they’re getting from an analytics effort, IT teams may struggle to justify the cost of implementing the initiative properly.

Deploying a data analytics platform via a cloud-based architecture can eliminate most upfront capital expenses while reducing maintenance costs. It can also rein in the problem of too many one-off tools.

Operationally, an organization’s return on investment comes from the insights that data analytics can reveal to optimize marketing, operations, supply chains, and other business functions. To show ROI, IT teams must work with stakeholders to define clear success metrics that tie back to business goals. Examples might be that findings from data analytics led to a 10% increase in revenue, an 8% reduction in customer churn, or a 15% improvement in operational efficiency. Suddenly, that cloud service seems like a bargain.

While quantifiable data is important, some benefits might be harder to measure directly, so IT teams need to think beyond just line-item numbers. For example, a data project might improve decision-making agility or customer experience, which can lead to long-term gains.

8. Changing technology

The data analytics landscape is constantly evolving, with new tools, techniques, and technologies emerging all the time. For example, the race is currently on for companies to get advanced capabilities such as artificial intelligence (AI) and machine learning (ML) into the hands of business users as well as data scientists. That means introducing new tools that make these techniques accessible and relevant. But for some organizations, new analytics technologies may not be compatible with legacy systems and processes. This can cause data integration challenges that require greater transformations or custom-coded connectors to resolve.

Evolving feature sets also mean continually evaluating the best product fit for an organization’s particular business needs. Again, using cloud-based data analytics tools can smooth over feature and functionality upgrades, as the provider will ensure the latest version is always available. Compare that to an on-premises system that might only be updated every year or two, leading to a steeper learning curve between upgrades.

9. Resistance to change

Applying data analytics often requires what can be an uncomfortable level of change. Suddenly, teams have new information about what’s happening in the business and different options for how they should react. Leaders accustomed to operating on intuition rather than data may also feel challenged—or even threatened—by the shift.

To prevent such a backlash, IT staff should collaborate with individual departments to understand their data needs, then communicate how new analytics software can improve their processes. As part of the rollout, IT teams can show how data analytics advancements lead to more efficient workflows, deeper data insights, and ultimately, better decision-making across the business.

10. Goalsetting

Without clear goals and objectives, businesses will struggle to determine which data sources to use for a project, how to analyze data, what they want to do with results, and how they’ll measure success. A lack of clear goals can lead to unfocused data analytics efforts that don’t deliver meaningful insights or returns. This can be mitigated by defining the objectives and key results of a data analytics project before it begins.

Tackle Data Analytics Challenges with Oracle

Even for businesses that have already embraced data analytics, technology such as easy-to-use and intuitive machine learning, self-serve analytics, or advanced visualization systems can present new opportunities to gain a competitive edge and anticipate future business demands. As such, business leaders must continue to invest in people and technologies to improve the use of data and integrate analytics-driven strategies into their culture for sustained growth and relevance.

Oracle Analytics is a comprehensive analytics solution with ready-to-use capabilities across a wide range of workloads and data types. A dedicated data analytics platform can help your business manage the entire analytics process, from ingesting and preparing data to visualizing and sharing results. Users can leverage industry-leading artificial intelligence and machine learning to help resolve tough operational issues, predict outcomes, and mitigate risks. Business leaders, meanwhile, can obtain faster, more accurate insights to drive confident, highly informed decision-making.

Plus, Oracle makes it easy to analyze data sets and apply built-in ML models with contextual data visualizations—all for a predictable monthly subscription cost.

Change is inevitable in data analytics, so new challenges will arise. By adopting these strategies, organizations can overcome fear of change and data overload and start using analytics as a catalyst for growth.

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Data Analytics Challenges FAQs

What are the main challenges of data analytics?

The main challenges associated with data analytics include collecting meaningful data, selecting the right analytics tool, visualizing data, improving data quality, finding skilled analysts, and creating a data-driven culture.

How is machine learning used in data analytics?

Machine learning (ML) plays a powerful role in data analytics by automating tasks, uncovering hidden patterns, and making predictions from large and disparate data sets. For example, data cleaning and sorting can be time-consuming, manual processes. Machine learning algorithms can automate these tasks, freeing up data analysts for more strategic work such as interpreting results and building models.

In addition, large data sets can hold hidden patterns and trends that traditional statistical methods might miss. Machine learning algorithms can analyze vast amounts of data to identify complex relationships and spot trend anomalies. Once machine learning models are trained on historical company data, they can predict future outcomes to help minimize customer churn, build targeted marketing campaigns, and set optimal pricing levels.


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