There are numerous assumptions made about business analytics that don’t reflect reality. Among the most common: New platforms will integrate easily with existing systems, outputs will provide clear directives on what a business should do next, and people across the organization will embrace the use of data and analytics. Unfortunately, these misconceptions can lead to frustration and derail a project before it has a chance to succeed. Here are some common pitfalls of analytics initiatives, along with suggestions on how to avoid falling victim to these mistakes.
6 Challenges to Implementing Big Data and Analytics
Big data is usually defined in terms of the “3Vs”: data that has large volume, velocity, and variety. Organizations dealing with big data are ones that generate – or consume – a constant stream of data from multiple sources that needs to be stored, processed, and managed on an ongoing basis. And as big data becomes an ever more essential part of the business toolbox, firms that aren’t comfortable employing it may be put at a disadvantage when it comes to understanding their customers, entering new markets, and troubleshooting their own internal processes. But even as more and more companies accept the need to tackle big data, many fail to realize the extensive training, organizational impacts, and legal implications that come with big data and analytics.
Here are six common big data and analytics problems that businesses run into:
- Data Science Skills Gap: Technology has outpaced talent, leaving many businesses struggling to make use of the analytics tools they’ve purchased. Software vendors may tout user-friendly interfaces, but a trained data scientist, and in many cases an entire team of them, can be an invaluable – or even necessary – addition to your team. If you don’t have headcount for a data scientist or if you want to supplement your existing team, it can be useful to leveraging outside resources.
- Organizational Change: In order to really harness the value of data insights, businesses need to be ready to make a dramatic shift in every department, from top leadership to sales to recruiting. Employees at every level need to be trained to basic competency on using and understanding analytics tools, and some ought to receive more advanced training.
- Data Security: Security is a big concern for companies with huge quantities of data that they don’t want released. When it comes to collecting data, privacy laws differ greatly between politico-geographic areas and are applied differently depending on data type and quantity. A data breach can leave companies and their clients open to identity theft, liability, and loss of competitive information, so security is obviously a big challenge that needs to be taken seriously.
- Effective Interpretation: Many companies struggle to make effective use of insights gained from massive amounts of data. Analytics are not definitive, and a significant amount of knowledge and interpretation goes into turning insights into actionable business solutions.
- Data Management: Where will you store your data? How will you determine if your data is accurate? How do you keep it consistent across the organization? Will it be compatible with software tools and processes that you already use? These are questions that will need to be answered on an ongoing basis once big data and analytics become a part of your business model.
- Lack of support: Once an analytics tool is rolled out, many companies don’t have the support in place to reap all of the benefits.
5 Solutions to Overcoming Big Data Challenges
While implementing big data and analytics at your company can be a complex and intimidating task, it may pay off in many ways. However, there are things you can do to mitigate the risks that come with taking on this kind of initiative. If implemented properly, safeguarding measures can actually be fairly simple and low cost.
There are ways that businesses can avoid or overcome big data and analytics roadblocks:
- Take Stock of Data Expertise: You may have employees who have experience analyzing large data sets, or it may be uncharted territory for everyone. Wouldn’t you like to know the level of data expertise at hand before spending money on a massive analytics initiative? Survey or interview your employee base to take stock of where your organization stands when it comes to big data. Or consider working with a partner that can fill your analytics expertise gaps in an “outsourced” or “service” model.
- Develop a Roll-Out Plan: It goes without saying that you will need a roll-out plan, as not everyone in your organization will see the value in a robust data and analytics program. A top-down approach is often the simplest and most effective, as team members will need to adapt to work cooperatively with leaders.
- Secure Your Data: Some of the most popular and effective methods of data security include identity and access control, data encryption, and data segregation. Limit the number of employees that have access to data by keeping it password locked and/or encrypted. Segregating your data into multiple databases can also secure most of your data if one or more of your storage methods is compromised.
- Finding the Right Analytics Solution. While the number of different analytics software programs available may be overwhelming, most will allow you to test-run their software before purchase. Define what your business hopes to get out of big data before trying out software. Choosing software suitable to your business needs will make it easier for your employees to effectively interpret the data.
- Effective Data Governance: Top leaders should decide on a data governance program before you embark on your data journey. This will guide all decisions on how you store, secure, and analyze your data. The best data governance programs also include an analytics governance aspect.
- Ensure Your Initiative Has Support: If your solution is built internally, make sure that the team is aware that it won’t be a “one and done” effort. They will still need to iterate and upgrade the tool to ensure that users are able to get the most out of it.
Online tools can also help make sense of big data. Data must be consistent and easily integrated among tools in order to realize the most value. Relying on common identifiers, such as the Dun & Bradstreet D‑U‑N‑S® Number, to establish a Master Data framework can avoid entity confusion. Using data effectively can be a huge boon to any business, so make sure to look for the best solution for your company, if you face any of the above common challenges.