Big data has become an indispensable tool for today’s top organizations in the continuing efforts to get to know customers better. The virtual tomes of big data coursing through any given company’s system offer the potential for vast and untold possibilities for a major boost in profits, improved customer satisfaction, and edging out the competition.
Is Your Company Ready for Big Data Analytics?
The depth of business-critical data pools has increased exponentially in recent years. With this abundance of data comes the challenge of properly reading and analyzing it in order to improve corporate decision-making. Increasingly, more and more businesses and service organizations have adopted a “big data” analytics model that employs advanced statistical models and techniques in the review of their information.
Big is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision-making, insight discovery, and process optimization. That’s why it lends itself well to analytical tools.
Advantages of Implementing Data Analysis
The analysis of your big data has been shown in many cases to have a dramatic impact on your profitability. The ability to leverage the trends shown by your data through the use of better analytical tools may provide you with the extra information and guidance that you’ve been searching for to consistently achieve the lofty standards that you’ve set for your organization. Big data analytics can help you by better identifying:
- Process and performance improvement opportunities
- Customer spending behaviors
- The effectiveness of your own internal processes
Perhaps the main selling point for adopting big data analytics is that you already have the intellectual resources needed to make it work. Every day, your organization collects informational and transactional data that can tell you all that you need to know about the efficiency of your operations and the preferences and habits of your customers and clients. Without the advantage of big data analysis, however, you may not be able to correctly identify how that information could be used to help drive improvements.
Yet, while nearly every business or organization is sure to have an abundance of intellectual resources, they may lack the physical ones to make big data analytics work for them. As the name implies, big data is just that: big. So, too, is the amount of effort needed in order to review it. Adopting such an analytical model without the ability to benefit from it may cause your organization more harm than good. However, failing to recognize the opportunity that this analytical model presents to you puts you in danger of having a competitive disadvantage in your market.
In order to reasonably gauge your organization’s ability to support a big data analytical model, you should ask yourself these questions:
- Do we have immediate access to the data needed for analysis?
- Do we have the resources to make effective use of the results of that analysis?
- Have we clearly defined the roles and responsibilities of those resources?
With an ever-increasing and impressive array of technology to accommodate big data, it has also become increasingly easy and inexpensive to collect, store, process, analyze and visualize this rich resource for long stretches of time.
You may be considering investing more time, energy and resources on exploring all the possibilities involved with generating value from big data analytics and data visualization to further your own business operations to a minor—or perhaps quite major—degree.
5 Factors to Consider Increasing Reliance on Big Data
While big data increasingly takes center stage for marketing, human resource, finance and technology teams in businesses around the globe, it is important to remember that this rewarding pursuit comes with its share of issues regarding big data privacy and compliance.
Let’s take a look at the top five considerations to make as you embark on, or continue, your exciting, rewarding and profitable adventures in big data.
1. Need for Higher Security
Businesses collect data from a variety of sources, such as laptop and desktop computers and smart devices like mobile phones and tablets; all culminating in the expanding IoT network.
This abundance of prized information is a huge responsibility for organizations in the modern business climate where cybercriminals abound and never tire of developing new ways to infiltrate systems and steal data. Therefore, as your collection of big data grows, so do your concerns over big data security.
It is more important than ever to learn about and comply with any governmental regulations, policies and standards related to your industry.
A few prominent industry regulations include the following:
- PCI DSS: This security standard requires compliance for all organizations that gather, store, process or transmit customers’ payment information.
- HIPAA: This act requires healthcare organizations, along with any business associates or third parties, adhere to requirements that protect the valuable patient data.
- GDPR: Designed by the EU, the GDPR is a uniform data security law instituted to protect EU consumers. As of May 2018, any business in any country that does business with EU residents is subject to the extensive requirements of the GDPR.
These are only a few of the many regulations and standards with which businesses in different industries must comply. Your auditing team can help you determine any standards and regulations that your collection of big data is subject to.
Additional data security issues, along with a key possible solution for each, include the following:
- Securing non-relational data through means like encrypting or hashing passwords
- Ensuring endpoint security with trusted certificates
- Preventing internal threats via proper authorization, access controls, and analyzing and monitoring data security in real-time
- Providing secure data storage with strategies like auto-tiering
2. System Integration for a Solid Big Data Environment
It is important to ask this key question as you launch your own big data project:
Is our organization’s current computing system able to accommodate the volume of big data we plan to import?
Even if your computing system has the storage capacity to contain all the big data you plan to collect, does it have the capacity to work with data to perform data analytics and data visualization? Many organizations work with systems that are out-of-date when it comes to dynamically manipulating data to turn it into the useful tool you have in mind. It is crucial that your organization invests in the right data architecture to facilitate the best use of your big data.
3. Employee Training
Big data is pretty much one of the new kids on the block in the information technology world, so finding and onboarding experienced talent may prove challenging at first. What’s more, this talent is not likely to come cheap.
Many businesses that are only beginning their work with big data enlist the services of consultants to offer the necessary expertise. Finding in-house data scientists often takes time since this key staffer must have excellent mathematical and computer skills, along with an uncanny ability to see patterns and trends in the data.
4. Proper Budgeting
Taking into account considerations already mentioned for security, staffing, and system integration, the costs involved with taking on big data can quickly spiral above your anticipated initial budget.
Although the costs involved with collecting and storing data are relatively low these days, thanks to cloud storage and hosting; the price of analyzing and visualizing big data is a fairly expensive matter. In the end, companies need to look at the long-term potential results to determine whether the initial investment in the best data infrastructure and tools is worth it.
Considering the fact that 92% of users feel satisfied with business outcomes by relying on big data architecture and tools, it seems like focusing on big data is an advantageous expenditure for any type of business from small and mid-sized businesses to large corporations.
5. Implementing Conclusions Gleaned from Data
Once you have built a secure and cost-effective environment for your big data, hired the perfect data scientist, and analyzed that data, it is important to know just what to do with that data to make it all worthwhile. Businesses spend millions of dollars collecting and analyzing data, so it is imperative for relevant parties to use those results in actionable and profitable ways. One key strategy that businesses employ is to ask good questions about a set of data.
Following are a few questions to ask to help ensure that your big data becomes the big investment you may be banking on:
- What do you want from your data?
- What are your Key Performance Indicators (KPI)?
- Where will your data come from?
- Are you sure about your data quality?
- What types of statistical analysis do you want to use?
Related article: Explore the Process of Data Mining to Discover the Best Techniques.
Plan Your Approach and Big Data Focus
The allure of big data is completely understandable, and so is the confusion surrounding it. If you are about to invest in your own big data environment, our team at I.S. Partners, LLC. is here to help.
Our clients are increasingly investing in big data and the tools that put it to highly rewarding use, and they need our help in ensuring data security to protect their customers, stakeholders and brand. We’ve helped them, and we want to do the same for you.
Editor’s Note: this article was originally published in 2018 and has since been updated for accuracy and timeliness.