Are we Ready for Big Data? Assessing Benefits, Challenges, and Software's Role

Raw data is a perpetually-growing challenge for businesses. With people generating 2.5 quintillion bytes of daily data worldwide, leaders are scrambling to get a handle on ingestion. There’s tremendous value hidden within this deluge of information. The trick is sifting through the noise. Organizations have achieved varied levels of big data readiness in the past decade. How exactly can they benefit, and what challenges must still be overcome?

Where does big data originate?

Big data stems from a litany of sources: our internet of things (IoT) devices, our smart devices, social media engagement—even that website form you submitted the other day. It might even be collected at the point of sale, both online and in person. It’s accordingly said that big data has three primary sources: 

• Social data

• Machine data

• Transactional data

For businesses, incoming data is also divided amongst two categories: internal and external. While customers and users account for the bulk of ingested data, a company’s own teams can meaningfully contribute. Having a fundamental understanding of origin is step one in piecing together a big-data strategy.

This information is collected both passively and actively. For example, making an online purchase is a very deliberate behavior. Users come in expecting to relinquish personal identifiable information. A conscious action ultimately leads to data collection.

However, internet-connected devices (e.g. Ring video doorbells or even smart appliances) upload video, audio, and even usage metrics to remote servers. This helps companies understand how their products are being used, and sometimes bolsters home security. The same is true when navigating websites, or any online entity that may monitor your activity—hence why privacy policies are ubiquitous and mandated.

Their consumption, however, is not. It’s crucial to note that individuals aren’t universally privy to all ways in which their own data is harvested. 

Big Data Benefits

Simply put, big data equals big opportunity—opportunity to learn more about users, customers, and any underlying patterns resulting from their activity. 

Customer retention is a prime example. Say you’re a keystone brand in the subscription space, like Netflix. There are competing options vying for your customers, including Hulu, Amazon Prime Video, and Disney+. This market saturation is threatening. Despite any loyal that brands have built, it’s hard for existing users to avoid taking a gander at emerging alternatives. Cause friction (via price hikes, poor customer service, or poor service quality) and user flight will skyrocket.

Seizing Opportunity

If companies can deduce why their customers are leaving, they’re much better equipped to halt the exodus. Surveys and other insight-gathering methods are revealing. These can lead companies to dynamically strategize—at a grander scale, or on a case-by-case basis. 

Overall, big data can be a powerful source of awareness for companies. Provided it’s deciphered properly, big data helps companies make better decisions and act based on evidence, instead of impulse. It’s thus possible to gain a competitive advantage. This is doubly true if companies partner with analytics specialists. Assuming the process is easy is a mistake.

Statista estimates that the big-data market will surpass a $77 billion valuation by 2023. There are massive incentives for companies to build cohesive strategies. What obstacles will they face on this journey?

Big Data Challenges

Not all data is created equally. Some is clearly organized upon collection, whereas other data is a jumbled amalgamation. This highlights the difference between structured data and unstructured data. The latter is particularly enigmatic; in fact, it’s a problem for 95% of businesses. What if you were given a list of unorganized addresses, names, and phone numbers? It would be incredibly difficult to draw connections between those data points. 

There’s also the notion that some data is ‘noisy’. Any data detracting from a greater set’s underlying value or meaning is deemed as such. Noise is confounding and makes true insight gathering laborious. 

Big data also has four attributes:

• Volume – there’s a metric ton of data out there

• Velocity – big data pools are growing larger every moment

• Veracity – big data accuracy can vary widely

• Variety – big data is diverse in source, structure, and context

Numerous considerations come into play when dealing with big data. Thankfully, plenty of technologies can alleviate analytical burdens.

Software and Technologies

Sorting through data is too arduous a process for human hands. It takes too much time when dealing with thousands or even millions of data points. Proper data consumption relies on automation. Technologies like artificial intelligence (AI) and machine learning (ML) supercharge automation at a fundamental level. Algorithms sort through data—hoping to uncover notable patterns sans influence, or instead meet pre-determined goals via ‘supervision.’ 

Algorithm design is a differentiating factor between third parties. A massive market of competitive analytics services exists. That same market grew by 14% from 2018 to 2019. Even larger gains are expected this year as the industry becomes entirely dependent on automation. 

Popular Options

External vendors appeal to companies sitting on mountains of data. Teams would rather let SaaS companies do the legwork instead of developing in-house solutions. 

Creating bespoke automation solutions is expensive and time consuming. Finalized solutions also require continual oversight. Consequently, the vast majority of internal automation campaigns fail prior to implementation. 

Where are businesses turning? Vendors like Spark—makers of Spark SQL—have a huge following in the business world. Apache Hadoop, Tableau, MongoDB, and smaller providers like Bellwethr excel at various stages of the analytics process. These vendors provide numerous benefits: 

• Data visualization

• Logical data storage

• Scalability when data stores swell

• Data mining

• Text and image mining

• Insight generation

Each has its own philosophy and compatibility with client ecosystems. Companies hoping to extract maximum value from their data must determine which aspects matter most, what technologies they’re comfortable with, and whose priorities align closest with theirs. 

Big Data is Here to Stay

Big data isn’t simply the flavor of the month. It’s not a buzzword that will lose luster, nor is it a trend that’ll waver with time. The more connected we become as a society, the more data we’ll generate as a result. 

Consider the Institute of Physics’ own findings, which state that an average user would take roughly three million years to download all of the internet’s data. Forbes also estimates that over 150 trillion gigabytes of data will require analysis by 2025. Consumers are driving this growth. After all, more than 67% of data today is generated by individuals as opposed to companies. That’s a potential treasure trove for well-positioned organizations.

Internal data management nonetheless remains essential. Projects produce impressive sums of data during their lifecycles. Choosing the right tool to manage this cloud-based information is essential. We have to strategize at all levels if we’re to take a byte out of big data’s challenges.

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Portfolio banner image courtesy of ScienceSoft.