8 Data Analytics Challenges (And How to Solve Them)

Thanks to the digital age, humans generate more data than ever. If you've been in the data analytics space, you must have heard statements like, "By 2025, there will be 463 exabytes of data available on earth, stored on all the servers and devices in the world." And by the way, exabytes come after gigabytes (GB), terabytes (TB), and petabytes (PB).

The problem is no longer about data generation. The need of the hour is data storage and data integration. Data-driven businesses worldwide face the same challenge: analyzing big data to make meaningful business decisions. Moreover, there's also the risk of analysis paralysis.

This article points out the 8 most common data analytics challenges reported by data scientists and managers. Along with that, we will list the ways organizations are smartly tackling those challenges.

8 Data Analytics Challenges and How to Overcome Them

1. Collecting the right data

Statista states 120 zettabytes of data will be created, captured, consumed, and copied in 2023. In general, 99.999% of the data sources are irrelevant to a particular company or project. You'd need 0.001% of the data for effective decision-making. The question is which 0.001%. That's what keeps data analysts awake at night.

Not a huge volume of data, but relevant data is at the heart of successful big data analytics. Incorrect or irrelevant data will affect the output quality, thus misleading the decision-makers.

How to collect the right data for data analysis?

The first step in any data analysis project is knowing the requirements and how the data is grouped. The exact type of data to collect and use varies from project to project.
Once data quality requirements are laid out, you can generate or use existing available data.
The data you generate within the company is called "first-party data." This tends to be the most reliable since you have absolute control over it.
Second-party data includes datasets shared by other organizations. Then the final type of data is third-party data. Think of Facebook audience data and Google search data.
After data collection, it must be grouped and organized for analysis.

2. Storing the right data

Data must be stored somewhere before it can be analyzed. The ultimate choice for data storage depends on the project, data structure, functionality, and the skills of the data analysts.

Moreover, analysts and managers also face issues from regulators regarding business data storage. Several countries have set data protection laws to safeguard consumers' sensitive information. HIPAA is a prime example of it.

Failing to comply with the guidelines will attract regulatory scrutiny and fines. And the onus of this non-compliance often falls on the project manager and the analysts working on the project.

How to store the right data?

The most basic decision regarding data storage you need to make is whether to opt for on-premises or cloud data warehouse.
Cloud storage is the most common and cost-effective option among data scientists. But there are certain cases where you'd benefit from on-premise servers.
Next, you need to decide on the database. Broadly speaking, there are two types of databases: traditional SQL and the latest noSQL. Each type has its pros and cons.
SQL systems like Postgres and Oracle are best suited for enterprise computing, while NoSQL databases are more suited for processing big data. So decide after weighing both.
You would also like to choose one between a data lake and a data warehouse. While data warehouses are suitable for storing structured data, data lakes allow you to store raw data too.
Lastly, you must comply with data storage laws and regulations. Also, adopt the best practices for data sharing.

3. Creating data handling policy and framework

A data handling policy is a protocol that employees need to follow when dealing with data. Why is this important? For security and compliance purposes.

You need to ensure everyone handling data is on the same page. The thing data managers struggle with is creating the handling policy. It's something that varies from company to company. Hence, it can't be purchased or borrowed. It has to be created and implemented in-house.

How to create a data handling policy?

Creating a data handling policy starts with understanding the company's data management systems.
Your team must understand the different types of data, their sensitivity level, and their corresponding risk level.
Public information is considered low-risk data with a low sensitivity level, while certain first-party data like social security numbers are of a high sensitivity level. You must classify data accordingly.
Once this is done, you need to specify who has access to which level of data and how the data should be stored, handled, and erased.
You'd also have to define the devices on which certain data can be accessed. Ideally, you wouldn't want sensitive data to be accessed remotely by anyone.
Data handling policy is a part of data security regulations like CPRA and GDPR. Thus, creating a policy that's in line with the regulatory guidelines is necessary.

4. Selecting the right data analytics tools

The amount of data has risen exponentially in the past few years. Keeping pace with it, we now have numerous data analytics tools. Instead of simplifying things, it has become one of the biggest challenges for data scientists. Picking the right data analytics tool is a challenge in itself.

How to select the right data analytics tools?

To pick the appropriate tools from the ocean of options, you need to answer the following questions:

What are the business objectives? Each automation and analytics platform is designed for a specific purpose. For example, Microsoft Excel is designed for basic calculations and statistical operations, while Tableau is a comprehensive business intelligence and visualization tool. Likewise, Google Analytics enables you to analyze visitors on your website, while Parse.ly grants a real-time view of content performance. Therefore, you must first define your business objective.
What user interface or visualization I'd prefer? Each analysis tool will have a different interface and data visualization. This is where the expertise level of the data consumers comes into play. Do you want to present the data across the business, including CEOs and CMOs? Or do you want the analysis work to stay within the data analytics department? For the first case, you'd want a tool with simplistic visualization (like graphs) and an effortless interface.
What's my budget? All data analytics tools are priced differently. For example, Metabase is a free platform, whereas IBM Cognos is a paid tool, even though both are business intelligence tools. So, you need to consider your budget and then go about buying the right Power BI tool.
Do I need customization? As already said, analytics tools vary greatly from one another. Although most tools grant a certain level of customization, some tools will have more customization options than others. Custom ETL allows you to dissect and present information as per your requirements. Therefore, inspect these features too.
Does the tool meet data guidelines' requirements? You must create data guidelines for your organization and ensure the analytics tool fulfills them. Also, ensure the tool meets the data security laws and guidelines. Since data will be shared with the tools, non-compliance can pose risks like data leakage and abuse.

It's time-consuming to set up the appropriate data flows between your business systems. But with Portable, data analytics teams can sync data from 500+ ETL connectors and their data warehouse with unlimited data volumes.

5. Lack of unified goals

The bigger the company, the more teams that work together. But it's not uncommon for these teams to work in silos with their own goals. For example, the sales team might have a particular sales target, while the marketing team will aim for a particular set of leads. If these two metrics (leads and sales) are not aligned, you'll see inefficiency within the company.

The same is the case for data science. Often, teams working in silos develop their own goals, independent of others. This not only reduces efficiency but also affects employee morale.

How to create unified goals?

To create goals for modern data science projects, you must follow the classic solution: collaborate more.
This can be done simply by having people work together rather than in silos.
Have them communicate explicitly and work on a specific analytics solution.
Administrative support from the top also helps in collaboration. Top executives must be willing to facilitate and enforce collaboration and actively monitor it.
Creating a 'mentoring culture' where leaders from a team mentor the members of a second team also helps with collaboration.

6. Handling too much data

You might think the more data you have, the better the results. That could be the case in a few instances (like machine learning projects), but that's not always the case. In fact, 65% of businesses reported in a survey that they have too much data for analysis.

In other words, they can't derive meaningful insights from all the data they collect. This is understandable since the cost of generating and aggregating data has decreased drastically.

Too much data also pushes data engineers into an "analysis paralysis" mode. This is where stakeholders overanalyze the data and fail to take the necessary action. This stagnates the whole process and affects the bottom line.

How to avoid too much data for analysis?

Analysis paralysis is common when projects are high stakes. These projects can make or break the company. Therefore, it's natural for managers to overanalyze stuff.
As per psychology, the root cause of analysis paralysis is anxiety.
When people are stressed, they will take too much time preparing, hoping for a better outcome. But contrarily, it backfires, and teams underdeliver.
To avoid analysis paralysis, executives must help data scientists cope with stress and anxiety.
Working in small batches also helps reduce analysis paralysis. Starting small allows you to get started while not having too much at stake.
Once teams start getting traction, they can scale with more confidence.

7. Lack of skilled resources

Companies of all sizes, including Deloitte and EY, are facing a data science talent gap. The number of skilled data scientists hasn't kept pace with the growth and proliferation of data.

As a result, many companies are getting by with limited talent pool. This leaves many organizations with an incomplete data science team. Safe to say, companies are facing the challenge of talent shortage in data science.

How to hire skilled data scientists?

One way to tackle the labor shortage is by allocating more budget to the data analytics team. With more budget, you can pay more to skilled data scientists and attract them into your organization.
If that's not possible, conceptualizing an in-house accelerator program helps.
These training programs train recent grads or people changing careers to data science. They are equipped with the necessary data science skills, at least at the basic level. This allows companies to scout and invest in future talent. This approach may take longer to deliver results, but it's sustainable for the long term.
Outsourcing data science tasks may also help bridge the talent gap. There's a list of consultants that specialize in data science. By outsourcing, you can get things done without investing in talent.

8. Being environmentally friendly

The use of computing resources has increased with the rise in data storage, retrieval, and analysis. Servers are now in operation more than ever. And unfortunately, these servers have a considerable carbon footprint. So, when you utilize computing resources, you're emitting carbon into the environment, contributing to global warming.

Consumers have made their preferences clear. They'd happily pay more for sustainable products that care about the environment. Business owners and managers, thereby, strive to make their companies environmentally friendly.

How to keep data science projects environmentally friendly?

One way to reduce carbon emissions from data systems is to use fewer computing resources. This includes using the server smartly and reducing repetitive processes.
Limiting data sharing and having fewer API integrations also help reduce carbon emissions. Both these activities increase computing resources. Therefore, avoid them as much as possible.
Use DCIM (Data Center Infrastructure Management) tools to monitor your server usage and detect inefficiencies. These tools help you analyze power and computing consumption in real-time.

These are eight of the biggest challenges data scientists face today. But by breaking them down to bite-sized opportunities, you can get past them.

As the modern data stack evolves, many of these obstacles will subside, but new ones will emerge.

As a data analytics practitioner, it's your duty to stay agile to solve emerging business challenges in creative and sustainable ways.