Top 5 big data challenges and how you can address them

“Big Data has arrived, but big insights have not.” ―Tim Harford, an English columnist and economist.

A decade on, big data challenges remain overwhelming for most organizations.

Since ‘big data’ was formally defined and called the next game-changer in 2001, investments in big data solutions have become nearly universal. However, only half of companies can boast that their decision-making is driven by data, according to a recent survey from Capgemini Research Institute.

Fewer yet, 43%, say that they have been able to monetize their data through products and services. So far, big data has fulfilled its big promise only for a fraction of adopters — data masters.

They are reporting a 70% higher revenue per employee, 22% higher profitability, and the benefits sought after by the rest of the cohort, such as cost cuts, operational improvements, and customer engagement.

What are the big data roadblocks that hold back others from extracting impactful insights from tons and tons of information they’ve been collecting so diligently? Let’s explore.

We will first back up to look at what big data is anyway. Then we will try to figure out what challenges with big data make data analytics and data science complicated. Vitali Likhadzed, ITRex CEO with more than 20 years of experience in the technology sector, will join in to share his insights!

So, what is big data?

Watching a recommended TV show on Netflix? Shopping on Amazon? Browsing Chrome? Clicking a cookie pop-up? Using a TikTok filter?

If yes, big data technologies are firmly a part of your life.

All of these services are collecting and processing massive amounts of diverse data known nowadays as big data.

In essence, big data is a buzzword standing for explosive growth in data and the emergence of advanced tools and techniques to uncover patterns in it.

• Volume: It’s petabytes, or even exabytes, of data
• Velocity: The pace at which data is flowing in is mind-boggling: 1.7 megabytes of data is created every second per person
• Variety: Big data is mixed data, including both structured and raw, unstructured data from social media feeds, emails, search indexes, medical images, voice recordings, video, and many other sources
• Veracity: A significant part of big data is associated with uncertainty and imprecision

Big data undergoes a few stages to deliver insights. These can be presented as follows:

Why has big data come into prominence?

From marketing intelligence enabling personalized offers to predictive maintenance, real-time alerts, innovative products, and next-level supply chains, leading companies that know how to deal with big data challenges reap enormous benefits across industries from data analytics and data science.

But big data is so massive, so messy, and so ridiculously fast-growing that it’s next to impossible to analyze it using traditional systems and techniques.

The hottest technologies of today — cloud computing, artificial intelligence, and more seamless analytics tools — have made the task accomplishable. There are a few problems with big data, though. Read on.

Challenges of big data — What stands in the way to a digital nirvana?

Despite new technology solutions deluging the market, a slew of big data problems drag down digital transformation efforts. Less than half of companies say in a new study from NewVantage Partners that they are driving innovation with data or competing on analytics.

Most companies (92%) cite people, business processes, and culture as principal big data challenges. Only 8% put down major big data barriers to technology limitations. What’s exactly the problem with big data implementation?

ITRex CEO Vital Likhadzed sat down with us to discuss common big data issues faced by companies and ways to fix them. Here is his insightful analysis that covers the five biggest big data pitfalls:

Big data challenge 1: Data silos and poor data quality

The problem with any data in any organization is always that it is kept in different places and in different formats. A simple task like having a look at production costs might be daunting for a manager when finance is keeping tabs on supplies expenses, payroll, and other financial data, as it should do, while information from machines on the manufacturing floor is sitting unintegrated in the production department’s database, as it shouldn’t.

Another major challenge with big data is that it’s never 100% consistent. Getting a detailed overview of shipments to, say, India can also be a problem for our plant in question, if the sales team handles local clients under the India tag, production uses the IND acronym while finance has gone for a totally different country code. The varying levels of data granularity they may apply for managing their databases only rub more salt in the wound of big data analytics.

Finally, data is prone to errors. The more datasets you have, the more likely you are to get the same data misstated with different types and margins of error. There can also be duplicate records multiplying challenges for your big data analytics.

Solution

• Building a data governance framework is a non-negotiable imperative if you want workable data. This framework establishes policies, procedures, and processes to set the bar for the quality of your data, make it visible, and install solid safeguards (if you by any chance don’t have data security and privacy on your radar, you should — non-compliance with regulatory requirements like GDPR and CCPA is punished painfully). It’s important to align your data governance with business needs. If you are in healthcare, for instance, it definitely should be centered around compliance with HIPAA or other industry standards.
  
  With robust data governance in place, you will be well equipped to address the quality and consistency challenges with big data by implementing master data and metadata management practices.
  
  A consolidation model is a good choice for managing master data (your key business data about customers, products, suppliers, or locations). In this approach, master data is merged from different sources into a central repository that acts as a single version of truth, or the “golden record.” This helps eliminate the duplication and redundancy problem with big data.
  
  For metadata (data about your data) management, you will need to build a data catalog. It’s essentially an inventory of all your data assets for data discovery. Advanced data catalogs incorporate business glossaries, run checks on data quality, offer data lineage, and help with data preparation. However, hard-and-fast validation rules are needed to ensure that data entries match catalog definitions. Both business and IT people should take part in defining them.
• Embed quality considerations into the setup of applications as part of managing your entire IT ecosystem, but define data requirements based on your use cases. It’s important. You should first identify your business problem or use case (in very specific terms) and determine what data you need to solve it. And only then requirements for data should be carefully considered.
• When working with data, organize it into several logical layers. This means that you should integrate, treat and transform your data into new entities step by step so that it reaches the analytics layer as a higher quality resource that makes sense for business users.
• Make use of technology innovations wherever possible to automate and improve parsing, cleansing, profiling, data enrichment, and many other data management processes. There are plenty of good data management tools in the market.
• The role of data stewards is critical. Data governance is not only about standards and technologies but in large measure about people. Data stewards are responsible for data quality, acting as a central point of contact in the organization to go to for all data-related issues. They have a down-to-earth understanding of data lineage (how data is captured, changed, stored, and utilized), which enables them to trace issues to their root cause in data pipelines.

Big data challenge 2: Lack of coordination to steer big data / AI initiatives

With no single point of accountability, data analytics often boils down to poorly focused initiatives. Implemented by standalone business or IT teams on an ad hoc basis, such projects lead to missed steps and misinformed decisions.

Any data governance strategy, no matter how brilliant, is also doomed, if there’s no one to coordinate it.

Even worse, a disjointed approach to data management makes it impossible to understand what data is available at the level of the organization, let alone to prioritize use cases

This challenge with big data implementation means that the company has no visibility into its data assets, gets wrong answers from algorithms-fed junk data, and faces increased security and privacy risks. It also wastes money as data teams process data without any business value, with no one taking ownership.

Solution

• Any data-powered organization needs a centralized role like the chief data officer who should be primarily responsible for spelling out STRICT RULES as part of data governance and making sure they are followed for all data projects. In fact, they should be applied to every IT initiative because in one way or another any IT initiative today will be related to data, whether you want to spin off a database, build a new application
  
  The role of chief data officer can be taken by a senior data master or by the chief information officer who has always been a perfect fit.
  
  The chief data officer is instrumental to setting the company’s strategic data vision, driving data governance policies, and adjusting processes to the mastery of the organization.
• Establishing data tribes, or centers of excellence, is also a very, very good idea. Such squads normally include data stewards, data engineers, and data analysts who team upand consistent data processes. They will help too with addressing the coordination problem with big data.
  
  To make your data tribe efficient, it is important you measure their performance by the number of big data use cases identified and successfully implemented. This way, they will be motivated to help other teams with extracting maximum value from new technologies and data the company has on its hands.
  
  Education is another key mission of data squads. A common problem is that many people just don’t want to learn new skills because learning can be challenging and uncomfortable. The data tribe keeps people engaged, educates them on how to use new tools and work use cases, and importantly lends a hand with changing their day-to-day processes.
  
  Make sure your data squad is doing the following:
  1. Looking for opportunities and gaps in processes across the organization for implementing AI business solution
  2. Incubating skills and sharing tribal knowledge through mentoring
  3. Cooperating closely with subject matter experts from business teams to identify pain points they are struggling with
  4. Asking business teams the right questions to understand clearly their KPIs and how data can help achieve them

Big data challenge 2: Lack of
coordination to steer big data / AI initiatives

With no single point of accountability, data analytics often boils down to poorly focused initiatives. Implemented by standalone business or IT teams on an ad hoc basis, such projects lead to missed steps and misinformed decisions.

Any data governance strategy, no matter how brilliant, is also doomed, if there’s no one to coordinate it.

Even worse, a disjointed approach to data management makes it impossible to understand what data is available at the level of the organization, let alone to prioritize use cases

The table below shows how changes in parameters increase design expenses.

	Basic design $15,000 or less*	Intermediate complexity $40,000	Complex design Over $60,000
User Roles	1	2	More than 2
Unique screens	10 or less	30-50	Over 70
Android or iOS	Android or iOS	Both Android and iOS	Smartphones, TV, tablets, and other gadgets

With no single point of accountability, data analytics often boils down to poorly focused initiatives. Implemented by standalone business or IT teams on an ad hoc basis, such projects lead to missed steps and misinformed decisions.

Any data governance strategy, no matter how brilliant, is also doomed, if there’s no one to coordinate it.

Even worse, a disjointed approach to data management makes it impossible to understand what data is available at the level of the organization, let alone to prioritize use cases

The table below shows how changes in parameters increase design expenses.

With no single point of accountability, data analytics often boils down to poorly focused initiatives. Implemented by standalone business or IT teams on an ad hoc basis, such projects lead to missed steps and misinformed decisions.

Any data governance strategy, no matter how brilliant, is also doomed, if there’s no one to coordinate it.

Even worse, a disjointed approach to data management makes it impossible to understand what data is available at the level of the organization, let alone to prioritize use cases

The table below shows how changes in parameters increase design expenses.