Many leaders at tech companies spent decades relying mostly on guesswork, assumptions and experience when making decisions. Now that big data solutions are so readily available, it’s easier to get confirmation of trends and feel more confident about how to proceed when dealing with a situation. Data analytics platforms can examine massive quantities of information much faster than humans could without help.
However, the success your tech business has with big data depends largely on your processes for handling it. Planning wisely can help your enterprise avoid pitfalls while extracting valuable insights from the information. Here are some vital things to keep in mind when working with big data and discovering insights.
Investigate How to Store Data Securely
Secure storage must be a top priority when you use big data. Cybersecurity incidents regularly appear in the headlines. The more information you store, the more significant the disaster could be if something goes wrong. Most tech companies encrypt information at rest and in transit, so that’s a smart place to start.
You could also only allow trusted individuals to access big data tools from up-to-date devices. Using automation is another wise strategy, especially as the overall amount of data rises with time. Human errors are virtually inevitable. However, you can set up parameters for automated security tools. They then provide alerts if any event happens that does not align with the created framework.
Understand How Big Data Supports Your Business Needs
Some tech company leaders feel compelled to invest in big data because competitors are. It’s natural if that’s a contributing factor, but you should look deeper and connect it to identified business needs. Otherwise, management of the information could become too haphazard. Big data platforms only pay off if you know how to use them to help your company meet its goals.
For example, it’s increasingly common to use big data in logistics so goods reach their destinations on time. Transportation management software lets users see the real-time location of products and when they should reach their destinations, along with any factors that could cause delays. Take a close look at your company’s operations and determine how big data could help overcome weaknesses while improving outcomes.
Learn About the Data Regulations Affecting Your Company
The General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are a couple of the data regulations your tech company may need to follow. However, specifics like which markets your business serves and your annual profits determine whether they apply to your enterprise. Once you confirm they do, take the time to determine how those regulations could affect your big data usage.
For example, companies must get consent from people before collecting their data. The GDPR also lets customers formally request that companies delete any stored information about them. If you feel unsure about how to make your data management practices align with any applicable regulations, consider getting advice from an experienced consultant. Having a reliable resource will help you avoid possible fines due to noncompliance.
Find Out How to Use Power Effectively in Your Data Centers
Perhaps you have on-site data centers that store and process your tech company’s information. If so, your management strategies should include how to ensure those facilities use power effectively. If you depend on an external data center, review its infrastructure. You may have heard references to power usage effectiveness (PUE). Calculating PUE requires dividing the total power entering a data center by the amount of energy used by IT systems.
The most efficient data centers have a PUE of close to one. Improving your metric requires finding the sources of excess energy usage and limiting them if possible. Using an uninterruptible power supply (UPS) and a power distribution unit (PDU) are common approaches used by many companies that want to prioritize efficiency while avoiding outages.
Be Transparent About Collected Data
Most people understand that companies gather their data, and it’s virtually impossible to use the internet in any meaningful way without relinquishing some details about themselves. An excellent approach to take is to never take more information than necessary. Doing that simplifies your management strategy. It’s also less likely to make people wonder, “Why do they need that information?” or feel suspicious of your company’s motives.
It’s also ideal to tell customers about your data principles. Firefox did that by releasing a public promise mentioning it classifies the information it has while maintaining rules for storing and protecting each type. The tech brand also reiterated that these principles were not new. However, given that some other brands are not as straightforward about data usage, Firefox tried to differentiate itself.
Appoint a Person or Team to Oversee Data Quality
Consistency is a crucial ingredient in any big data plan. A <href=”#51a0d2013ca8″>common mistake companies make is not choosing a person or group to set standards for data quality. Imagine the discrepancies that could happen if the people inputting information did not adhere to a universal format or have a way to check for duplicates. That makes it more likely that the results generated by a big data platform could contain errors.
Once there are established standards for what makes data high-quality and ready to use, the responsible person or parties can get involved in an organization-wide effort to train the employees who handle it. They can also uncover shortcomings that may compromise the information’s usefulness if not addressed.
Keep Everyone Informed of Changes
You’ve probably gotten several emails from tech companies this year that mentioned upcoming changes to data-related policies. Maybe your company needs to begin collecting information differently, or perhaps it added a segment in its policy that applies to people in some markets, but not others. That second scenario is most common if you’re part of a tech company with an international reach.
Whenever you’re about to start managing data differently, inform everyone first. That way, they have time to decide whether the changes affect them. Facebook had to take that approach when the GDPR came into effect. People who did not consent to the upcoming changes had to stop using the service. When you keep everyone up to date, they’ll be more likely to trust and continue associating with you.
Go with an Adaptable Approach
Besides following the tips above, remember that the way your technology company treats big data will likely evolve along with its needs. Some things — such as security and user privacy — will remain important.
However, you may use data in new ways over time or invest in a broader assortment of tools to work with it. Let those changes serve as cues to review your internal big data management strategies. Adjust them when necessary so they remain current, applicable and accurate.
It may take longer than you expect to iron out how your company should use and handle big data. That’s OK. It’s more important to take your time developing a thoughtful strategy than to rush to create one that does not work as well. These tips will help, and you’re sure to gain valuable insights while diving into the process.
- How to Improve Your Tech Sales in Q1 2021 - December 15, 2020
- Cryptocurrency’s Huge Impact on Businesses in 2021 - November 12, 2020
- How Your Tech Business Should Handle Big Data - October 8, 2020
3 thoughts on “How Your Tech Business Should Handle Big Data”
Pingback: How Your Tech Business Should Handle Big Data – IAM Network
Pingback: How Your Tech Business Should Handle Big Data – TechSpective – IoT – Internet of Things
Yes you are right…Big Data carries a whole new world of opportunities for businesses all over the world. Obviously, ingesting and capturing large volumes of data is a tough task. However, the solution you receive once you finish the toil of generating insights is worth the wait! Big Data Analytics, the solution that we are talking about, is the detailed analysis of productive patterns and correlations extracted from the stored data.
Comments are closed.