Big data is an immense field of opportunities for companies. Insights from big data can identify and solve problems within your organisation, provide insight into the customer's lifecycle, and inform ways to increase business and sales, but it comes with significant challenges.
The extensive amount of data generated every day keeps growing. As a result, companies have more available data than ever to inform their business decisions. But, this vast amount of data can bring as many challenges as solutions.
To be valid, data needs to be tracked, managed, cleaned, secured, and enriched throughout its journey inside your organisation to yield the most effective results. This article will cover some of the leading significant data challenges and solutions for how your company can overcome them.
Shared big data challenges
The specific challenges you'll likely face with big data will depend on your company's industry, tools and infrastructure, and the types of data you're dealing with. Still, the following core problems tend to show up repeatedly when you manage data.
Difficult to find the data you need.
The first challenge is that big data is significant. There's data for everything — e.g., customers' interests, website visitors, interaction data, conversion rates, churn rates, contact data, company data, and financial data.
While much of that data is beneficial, huge chunks aren't relevant for your company. And, with the sheer amount of data and information available, it can be hard to decide what is valuable for your company and what isn't.
This problem typically arises if data enters your organisation unfiltered and unstructured through various channels.
You're collecting inaccurate and outdated data.
Suppose you have too much data in your databases. In that case, it's possible you've inadvertently collected inaccurate data or that some of your data is invalid.
This problem usually starts at the collection process of your data lifecycle and is especially prevalent if your company collects data from many different sources and formats. If your data collection isn't standardised across all your channels and touchpoints, you can encounter problems when analysing the data and extracting insights.
This data can also be collected from apps that don't talk to each other or are viewed by several teams that don't have access to the full view or whole picture and analysed without safeguards to ensure the data's validity, quality, or security.
So, in essence, collecting insufficient data can lead to low standards of accuracy and quality. And, if you can't trust your collected data, you can't trust the analysis or the recommendations based on it.
Your data is stored in silos.
Organisational silos are one challenge for companies, and data silos are another when dealing with big data.
If all of your data is stored in separate databases that don't "talk" to each other, you've got data silos.
This means your teams aren't all looking at the same data but only have access to a snippet that doesn't tell the whole story.
Suppose your teams can only see a portion of the data. In that case, it usually leads to poor execution — it can be why your sales and marketing teams are misaligned or your customer service department misinterprets some customers' needs.
Without a 360-degree view of your data, figuring out how to build maximum extract value isn't easy.
Download below the benefits of a silo-free company and how to break silos down.
Data security and protection.
More data means more risk for security breaches. This problem is aggravated when data is less organised. As your company grows and you add new applications and tools to your tech and software stack to make sense of your data, there's a high probability of security laps.
You might want to consider some of the following threats to your data security:
Fake data generation.
- If you gather data from multiple sources indiscriminately, you might be collecting fake data. Invalid and fake data will affect any analysis you can get from it.
Unsecured data sources.
- Gathering data from channels that aren't secure means that your systems are more vulnerable to external infiltration and possibly even malware.
Unprotected stored data.
- When you store data you've gathered without any safeguards — such as access control, firewalls, and encryption — this data becomes vulnerable to issues such as malware, leaks, and data harvesting. This can be highly damaging to your company's business, not to mention your contacts and customers' privacy.
Non-compliance to privacy laws.
- Without a proper approach to ensure compliance with data protection laws — including protecting your data from actors with bad intentions — there's a much higher risk of exposure.
- In addition, without tracking and standardising all the channels through which you gather data, you can't ensure that users are providing appropriate consent.
Shortage of qualified big data analytics staff.
Many companies have problems finding qualified people to organise, manage, and analyse big data.
The tolls and technology around big data advance rapidly, but there aren't enough people to operate the tools and technology at an expert level. It's challenging to collect, manage, and build actionable reports from big data if your team doesn't have the know-how.
To create a big data approach.
To tackle the challenges of big data analytics that your company can encounter, perhaps you have noticed a pattern: They stem from a lack of structured processes to collect, manage, and analyse data.
By creating a proper data approach that outlines who handles all data, where the data comes from, where the data goes, and how the data moves within your systems and tools, you'll be in the desired position to derive actionable insights and create positive organisational change.
Now, let's review some big data best practices to follow.
Audit your data management process.
It's good to audit your current data management processes. Look at all apps in your software stack that collect data, such as your sales enablement, CRM, email marketing, CMS, and lead generation tools.
Some of these tools and processes might have been implemented when your company was at a different maturity stage, which means that they might not be a good fit for where you are now.
An excellent big data approach starts at the collection or the creation stage. First, ensure that all data entering your systems is accurate and valid. Also, make sure that your forms only accept valid email addresses and phone numbers with the correct number of digits.
You must triple-check that bots are not entering data, use, e.g., security technology like reCAPTCHA, and that your users provide full consent for you to store and handle their data. So again, compliance with data protection and privacy laws is crucial.
Provide adequate training.
If you can't dedicate a person or team specialising in managing data for you, make sure your existing teams that handle it daily know what to do.
This can involve providing data management and analytics courses, running data management boot camps, and training in the tools you use. If it's not feasible to hire new people to handle data — or if you can't find the talent — it's essential to keep your whole team up to speed to reduce human error.
That said, your data analytics doesn't have to be complex. Like Tableau, many tools make it easy for anyone to easily access, analyse, and make decisions based on data.
Implement a sound data management approach.
After you have audited your current processes, you will hopefully have a much better idea of what works for your company and organisation and what doesn't when it comes to data management. Take note of what areas need improvement and which are doing well.
Now is the time for you to outline your new data management approach. Your big data solution must fit your company now and in the future. If not, you'll run into problems as you scale up.
Cleaning up your databases is the first step in this approach.
Next, you should scan your databases and erase all outdated, duplicated, and invalid data.
So, make sure you build the best tech stack to manage and store data, introduce common company-wide standards for data entry and maintenance, back up all your data, and choose integration platforms to ensure your databases are connected and play sufficiently together.
Integrate data for enriched databases.
Integrating your databases is one of the essential things you can do to maximise your big data usage. Without integrations, you will always have data silos, a misaligned organisation, and the wrong output.
Besides, the world's best software or tech stack will never be 100% effective if it's not integrated. The most successful companies run with tools integrated in real-time. That enables them to have an accurate, updated, and 360-degree view of all aspects of the company.
There are a few ways to integrate your databases:
- Cover everyday use cases to connect two tools. You'll have to decide if the native integration offered by your app suits your company's particular integration needs.
- These integrations are tailor-made for everything your company needs from an integration solution; however, they are expensive to develop and require colleagues with specialist knowledge and skills.
Integration Platform as a Service (iPaaS).
- Third-party vendors provide integrations between hundreds of apps.
- With only one subscription, you can build bridges between multiple apps and manage all of your app connections in one place.
When you don't have any native integrations, some companies choose an iPaaS tool to integrate their software stack as the most comprehensive and cost-effective solution. These tools, e.g., Zapier and Automate.io specialise in trigger-action and one-way data pushes between apps.
An integration tool automates huge parts of your data management process, reduces the need for manual data entries, unifies data formats, and reduces the chances of human errors. It is also a big help to ensure your security and compliance with data protection laws.
An essential part of your big data approach is deciding where and for whom the data shall be accessible. Your data integration is the most reliable way to achieve this and ensure that the data flows correctly between your applications and software.
Want some 1-1 advice on how to use data as a natural way of working? Feel free to book a free consultation with me.