Insights

The Top 3 Data Challenges Businesses Face (and 4 Ways to Combat Them)

95% of businesses say managing unstructured data is a problem for their business...and nearly half of IT decision-makers fear their infrastructure won’t be able to handle future data demands. If every organization relies on data, why is accessing and using it such a barrier for so many? With the big data landscape growing so quickly, understanding the complexities surrounding successful adoption is crucial.

Businesses without a holistic data strategy in place (or at least a plan to put a strategy in place) will experience lost revenue, security breaches, missed opportunities, business inefficiencies, and customer dissatisfaction. This is a fact. Keeping on as you have been while other businesses undergo data transformation isn’t staying in stasis, it’s moving backwards.

Take a moment to think about your org. Where does your data come from? Who uses it? How do you store and access it? How do you know you’ve got it all? How do you clean and validate it? How do you keep it secure?

Data is vast. Data is diverse. Data is complex. To achieve your goals and mitigate your business’s security risk, you must conquer these three challenges.

Challenge #1: Data is scaling, faster and bigger.

90% of data was created in the last two years, catalyzed by the pandemic and propelling the internet into the zettabyte era. By 2025, 463 exabytes of data—that's 463 million terabytes—will be created every single day. Data grows exponentially and will continue to scale, whether your org is ready or not.

All that data is up for grabs, but it’s a murky pool your systems are reaching into. With the sheer volume of data floating around, how do you gather what you need (and what you want to know) without opening the dam too wide or clogging up the works?

The first step is determining what data is necessary, desirable, and nice to have across the organization, along with a list of what to exclude. Given these parameters, data scientists and data engineers employ filters to capture only relevant and required data. AI and machine learning can automate the verification process over time, studying the data a business uses most and ensuring the data that passes the filters is accurate and in the correct format for use. Data mastery is key to storing and finding the right information upon a report request—without it, users must manually sift through data for what’s important before analysis can even begin.

Now let’s talk about one of the biggest data trends and biggest expenditures for 2023: data security. Keeping your company data secure in the hands (and screens) of the teams (and machines) that use it is imperative, and updating passwords a few times a year won’t cut it. Your company must incorporate multiple procedures to ensure data remains secure at all times. Backup recovery, MFA, data encryption, secured wireless networks, software updates, and employee defense protocol…employing these helps orgs across sectors ensure that best data practices are in place for all workers, all the time, regardless of location. For especially sensitive data like health and financial records, confidential computing is an emerging option for putting data to use securely and at scale.

It's important to note that while winnowing down the available data to avoid distractions and malicious entry is important to managing data scale and security, it won’t be enough over the years as information multiplies exponentially. If you’re not already in a cloud environment that can expand with your data, you’ll need to start your transformation with a robust cloud data platform.

The importance of data security and corporate data-driven strategy to handle scale is intuitive, but it’s also complex, requiring successful team adoption and procedures. That’s especially important as we move into the next challenge of data.

Challenge #2: Data is growing more diverse, all the time.

80% of data is unstructured—it's images, documents, online reviews, handwritten notes, geospatial data, travel expenses, physical receipts, and more. Social networks, productivity and collaboration tools like Miro and Notion, open-source aggregates, podcasts, RSS feeds...new formats of information appear every year. Look at a small sample of the data created every minute of every day in 2021:

If you’re still relying on spreadsheets to make your decisions, you’re missing a wealth of data that could give you valuable insights about customer, market, and employment trends. Over the past few years, every sector has trended toward consumerization, acknowledging that modern customers expect an “unreasonably easy” experience whether they interact with Starbucks, Sutter Heath, or state government.

But customers don’t just share their experience via Yelp reviews. Understanding sentiment requires analyzing a person’s social posts, the frequency and amount of time spent interacting with your offerings, comparable data for people like them, and more. Regardless of what sector an organization works in, more diverse data is helpful...but only if it can be cleaned, standardized, maintained, and accessed easily when you need it.

So how do businesses get there? A robust data platform and automation are the keys. Conquering data diversity starts with finding and capturing the right third-party data from pinpointed sources of truth. Then, AI tools like Robotic Process Automation (RPA), Optical Character Recognition (OCR), and more can parse and validate that data. As an example, automatic verification and machine learning took one of the nation’s largest insurers’ provider data from 36% highly accurate to 96% highly accurate in a matter of months.

The data lifecycle is circular and always evolving—which means opportunity also grows. Like the fable of the ant and the grasshopper, investing the work now (before the next social platform fires up) will reap benefits for the tougher seasons ahead. Putting machines to work leaves an org time to test, evaluate, and repurpose data for the initiatives that drive growth, revenue, and better decision-making.

Challenge #3: Data is disconnected. 

Data silos are a huge difficulty for most organizations. This problem comes in three main forms:

·  2 out of 3 organizations have shadow data repositories, storing data that is invisible to the tools used to monitor and log data access

·  70% of businesses are unable to provide a comprehensive view of a customer

·  80% of businesses report moderate to high degrees of siloed data within their organization

When organizational data is segmented and stored separately, the insights gained from the information the org collects are fragmented at best. For a successful digital transformation, leaders need a full data picture. Without capturing a full and holistic view of data across the org, the results will be inconclusive and incomplete.

Siloes and shadow data also indicate other barriers to transformation. Specifically, they indicate challenges in org-wide communication and practice standardization. This is why organizational change must happen across process, practices, and people—to share information properly and make the business thrive, everyone must use the same rules of engagement and transparency.

On the other side of the shop, better customer data means better predictions for your business. As demand increases for fast, personalized, and emotional experiences, data helps anticipate their wants and needs. A customer’s profile may change multiple times over the course of their lives. Data helps you adapt to those changes while providing valuable insights into their behaviors. The demographic, transactional, economic, and even social media analytics living within your data gives you the understanding you need to make better business decisions.

To combat flawed customer insights and internal data silos, set consistent standards and acknowledge data strategy as an ongoing process. Incorporate data expectations and best practices across your teams, in addition to developing a centralized platform where your data will be continuously updated and stored. Disconnected data harms everyone: you, your employees, and your customers.

 

The scale, diversity, and disconnect are data are major barriers to overcome org-wide, but they are necessary to address. If your data strategy is lacking now, it will be further behind tomorrow. As data constantly evolves, businesses must take action to stay afloat. Over time, the data strategy you invest in now will give you the predictions and insights needed to optimize, grow, and scale in the years to come.

These challenges are mountains on the road to transformation, but they’re not insurmountable if you’ve got a good map. What’s holding you back from stepping into a data-filled journey to transformation? Start small: Take our Future State of Data Workshop to find out where your organization stands, identify your future state, and figure out how to get there on solid footing.

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Russ Whitman

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