The field of data analytics is a promising prospect but also a complex one. Big data adoption has revolutionized the way many businesses operate, but with this disruption come new questions about security. If you’re thinking of adopting data analytics, you need to consider several logistics and safety concerns.

You have to think about more than the potential gain before adopting a large-scale data analytics system. Data analytics is a complicated science with a lot of influencing factors that could drastically affect its efficacy. If you’re going to implement it, you’re going to need a robust plan to ensure you do so securely and effectively.

Here are seven key factors to consider for data adoption and protection.

1. Risk

Before you adopt an analytics system, you need to make sure you’re prepared to handle its risks. The most prominent hazard of this field is data security, but there are others to consider as well. You need to consider if big data adoption is even a worthwhile business venture for your company.
Data analytics is often an expensive and complicated process, so you should compare the monetary and logistic costs against what you can gain from it. Do you have the necessary security measures to prevent data breaches? Do you have the resources to implement analytic systems with minimal risk? You should consider these questions before moving forward.

2. Flexibility

Data science is an evolving industry. The world of data analytics has seen numerous hurdles and developments already, and you can expect more in the coming years. Given how significant an undertaking big data adoption is, you should ensure your system is flexible.

Your analytics system should be easy to shift or grow. You will likely see an increase in data sets over time, so scalability is a critical concern. On top of preparing for growth, you should consider different security threats that could arise and how you can change your methods to account for them.

3. Storage

As the name implies, big data involves working with a considerable amount of information. You’ll need somewhere to store this data. Depending on the size of your data sets and the resources of your company, you could consider on-premise systems, cloud adoption or a hybrid solution.

Every storage method comes with its share of benefits and disadvantages that you need to consider. You should also take the different security requirements and options of each system into account. Your security measures on the cloud won’t be the same as those needed for an on-premise solution.

4. Accessibility

The data you collect is of limited value if it’s difficult to access. The whole point of data analytics is to generate actionable insights and acting on the information is a lot easier if more people can access it. The more accessible your big data systems are, the more useful they will be.

Your big data should be available to more than your data scientists and analysts. To enable more people to access it, you should also make sure your systems are intuitive and user-friendly.

5. Data Sources

This one should be obvious, but you should consider the sources from which you’ll gather data. What you’ll be able to do with your data analytics depends on what kind of data you’re collecting. Think of what sources you already have at your disposal, as well as potential future sources you could incorporate.

As technology expands, you’ll have more available data sources. Innovations like the Internet of Things have provided a wealth of new information companies couldn’t access before. This availability can help you by gathering varied data sets, but can also flood you with irrelevant information.

6. Workload

Depending on the size of your company or your data sets, your analytics solution might have a substantial workload. If you’re going to be rummaging through vast amounts of data, you need to have a system that offers the necessary processing power to do so efficiently. If the system’s workload is too massive, it could lead to slowed operations or, more importantly, security breaches.

This area involves two considerations: the capacity of your system and the size of your data. You may not need to work with extensive data sets, which means you could opt for a smaller, more affordable solution. If you’re working with significant sizes, you’ll need a solution that can handle it.

7. Data Governance

The location of your business may affect your data analytics solutions. A growing number of privacy laws like the GDPR are going into effect, so you may have to comply with legal regulations on how you collect and implement data.

No matter what the specific laws are in your area, you should hold privacy as a chief concern. Conducting regular privacy and security audits may be a worthwhile endeavor. If you operate in an area with stricter privacy laws, you may consider seeking out legal consultation.

If you take the right steps, big data adoption can be a highly profitable venture. You stand to gain significant profits and insight from data analytics, but only as long as you consider these key concerns carefully.

Jenna TsuiAbout the author: Jenna Tsui is a Texan tech writer who loves to learn new trends in data science, AI, and machine learning.

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