5 challenges related to Big Data and how to solve them
Big Data is the next big area of opportunity for businesses. Insights derived from data can help improve the customer experience, identify and solve problems within the business, provide insights into the customer cycle, and identify ways to increase revenue, among other things.
Not to mention that the amount of data generated every day is constantly growing -. in fact, data production in 2020 will be 44 times greater than in 2009. As a result, companies have more data than ever before to inform their business decisions.
However, this massive amount of data brings with it many challenges, such as where to even start? To be useful, big data needs to be tracked, managed, secured, and enriched throughout its journey within your organization for the most effective results.
In this article, we'll explore some of the key challenges associated with Big Data and how your organization can address and overcome them.
5 common challenges with Big Data
1. you can not find the data you need so easily
The first problem many businesses run into is that Big Data is, well, big. There seems to be data for everything - customer engagement, website visitors, conversion rates, churn rates, financial data, and more.
While much of this data is extremely useful, there are large chunks of data that aren't exactly relevant to your business. And with the sheer amount of information available, it can be difficult to decide what's valuable to your business and what isn't.
This problem typically arises when data enters your organization unfiltered and unstructured through multiple channels.
2. they collect inaccurate and/or outdated data
If you have too much data in your databases, it is likely that you have inadvertently collected inaccurate data somewhere along the line, or that some of your data is no longer valid.
This problem starts with the capture process of your data lifecycle and is especially prevalent if your organization collects data from a variety of different sources and formats. If data collection isn't standardized across all channels, you can run into real problems when you need to analyze and gain insights from the data.
This data is also collected from multiple applications that don't always "talk" to each other, viewed by multiple teams that don't have access to the big picture, and analyzed without any safeguards to ensure data quality, validity, and security.
Essentially, poor data collection leads to low standards of quality and accuracy. And if you can't trust your data, you can't trust the analysis you get from it.
3. your data is stored in silos
Data silos are another major problem that can arise when dealing with large amounts of data.
If all your information is stored in separate databases that don't communicate with each other, you're dealing with data silos. This means that your teams aren't all looking at the same data, but instead have access to a limited slice that doesn't tell the whole story.
If your teams can only see a portion of the data, it can lead to poor execution - this could be because your marketing and sales teams are misaligned or your customer service department is misinterpreting a customer's needs.
Without a 360-degree view of your data, it's difficult to figure out how to create accurate, trustworthy reports and get the best value from them.
4. data security and data protection are overlooked
More data means more opportunities for security breaches. This problem is exacerbated when that data is less organized. As your business grows and you add new tools to your software stack and adopt new technologies to make sense of your data, the likelihood of security breaches increases. Consider the following potential threats to your data security:
- Generation of fake data. If you indiscriminately collect data from multiple sources, you may be collecting fake (and therefore invalid and potentially harmful) data. Fake and invalid data will compromise any analysis you can gain from it.
- Unsecured data sources. Collecting data from channels that are not secure means your systems are more vulnerable to external infiltration and possibly even malware.
- Unprotected stored data. If you store the data you collect without any security measures - such as encryption, access control, and firewalls - that data becomes vulnerable to issues like leaks, malware, and data harvesting that would be extremely damaging to your business, not to mention your customers' privacy.
- Non-compliance with data protection laws. Without a proper strategy to ensure compliance with privacy laws - which includes protecting your data from bad actors - it's at a much higher risk of being compromised. Furthermore, without tracking and standardizing all the channels through which you collect data, you can't ensure that users give their full and explicit consent.
5. there is a shortage of qualified personnel in big data analytics.
It is common for companies to have trouble finding qualified people to organize, manage, and analyze large amounts of data.
The technology and tools around Big Data are evolving rapidly, but there aren't necessarily enough people to operate it at an expert level. It's much harder to collect, manage and create actionable reports from big data if your team simply doesn't have the expertise.
How to create an effective strategy for Big Data
We've looked at the potential problems your business may encounter when dealing with big data, and you may have noticed a pattern in all of them: They stem from a lack of structured processes for collecting, managing, and analyzing data.
By developing a solid big data strategy that clearly outlines who handles the data, where it comes from and goes to, and how it moves within your systems, you'll be best positioned to derive actionable insights and drive positive organizational change. Let's look at some data handling best practices to follow.
1. review your current data management process
To start, it's a good idea to review your current data management processes. Look at all the applications in your software stack that collect data, such as your CRM, your email marketing application, and your lead generation tool.
Some of these processes and tools may have been implemented when your business was at a very different stage, which means they may not be a good fit for where you are now.
A good big data strategy starts at the collection or creation stage. Ensure that all data entered into your systems is correct and valid (e.g., make sure your forms only accept valid email addresses and phone numbers with the correct number of digits).
Also, triple check that no data is being entered by bots (you can use security technology like reCAPTCHA use) and that users give their full consent to the storage and processing of their data. Compliance with data protection and privacy laws is essential.
2. provide adequate training for your employees
If you can't have a person or team that specializes in managing data, make sure your existing teams that handle it on a daily basis know what to do.
This can include taking courses on data management and analysis, running data management boot camps, and providing extensive training in the tools you use. If it's not possible to hire new people to handle data - or if you can't find the talent - it's important to keep your entire team up to speed to reduce the occurrence of human error.
3. implement a sound data management strategy
After auditing your current processes, you'll hopefully have a much better idea of what works and what doesn't for your organization when it comes to data management. Pay attention to which areas need improvement and which are running well.
With this in mind, it's time to design a new data management strategy. Your data solution must fit your business now, but also in the future. Otherwise, you'll run into problems again as you scale.
Cleaning up your databases is the first step in this strategy. You may need to scan your databases and delete all obsolete, duplicate, and invalid data.
Then build the best technical stack to store and manage data, adopt enterprise-wide standards for data entry and maintenance, secure your data, and choose integration platforms to ensure your databases are connected and work well together.
4. Integrate data for enriched databases.
One of the most important things you can do to ensure you get the most out of big data is to integrate your databases. Without integration, no matter how good your data plan is, you will always end up with data silos and misaligned departments.
Furthermore, the best software stack in the world will never be 100% effective if it's not integrated. In fact, the most successful companies operate with tools that are integrated in real time, giving everyone an accurate, updated, 360-degree view of every aspect of the business.
There are a few options for integrating your databases:
- Native integrationscreated by the SaaS provider of the tools you are currently using. This type of integration covers the most common use cases for connecting two tools, and they are often one-way data pushes. You will need to determine if this meets your organization's particular integration requirements. In addition, each connection is managed separately, so there may be some overlap.
- Custom integrations Are created by an internal team. These integrations will be customized for whatever your business needs from an integration solution; however, they are expensive to create and require staff with specialized skills.
- An integration platform as a service tool (iPaaS). These third-party providers offer integrations between hundreds of enterprise apps. With a subscription, you can build bridges between multiple apps and manage all your app connections in one place.
For many companies, choosing an iPaaS tool to integrate their software stack is the most comprehensive and cost-effective solution. Examples of these tools include Tray.io or Automate.io, which specialize in trigger actions and one-way data pushes between apps, and PieSyncwhich provides real-time, bi-directional synchronization for integrating customer data between a variety of tools.
An integration tool automates large parts of the data management process, reduces the need for manual data entry, standardizes data formats, and reduces the risk of human error. It can also be a great help in ensuring security and compliance with data protection laws.
A critical part of your big data strategy is deciding where and to whom the data is accessible. Data integration is the most reliable way to achieve this and ensure that data flows correctly between all your applications.