The majority of modern-day enterprises are overwhelmed with all the data generated by internal systems as well as external platforms. Yet, only a few are leveraging this digital information to its utmost potential.

You see, typically, enterprises tend to deal with siloed, unstructured, and inaccessible data that is pretty much useless unless processed effectively. As you can imagine, within this data, lies a lot of untapped value that could drive businesses forward.

So, how can organizations spot the insights that are hidden away within their digital assets? Well, they can turn to enterprise data science, which is precisely the subject that we’re going to focus on today. Let’s dive into it.

When Do You Need Enterprise Data Science?

When Do You Need Enterprise Data Science?

First, let’s answer the “What is enterprise data science?” question. In short, it is a holistic approach towards leveraging the digital information that is present across the entire organization. It isn’t a one-off project that aims to solve a single problem. Rather, a strategic change that impacts the performance of the entire business in order to bring benefits for the long term.

Typically, enterprise data science goes hand-in-hand with cloud computing. This is because the latter facilitates work with larger datasets and the deployment of software solutions. Moreover, with the increase in Big Data, storing information on the cloud ensures that it stays backed up in a secure location.

Discover how Velvetech Performed Cloud-to-Cloud Infrastructure Migration

So, how do you know that it’s time to leverage your enterprise data and embrace analytics? Here are some key indicators:

If you’re facing any of the issues presented above, incorporating data science into your enterprise is likely the best step for you to take in 2022. Especially when data-related initiatives are some of the top tech focuses for businesses in the coming year.

The truth is, with the help of data lakes and warehouses, machine learning algorithms, and a comprehensive data architecture, you can begin applying all kinds of data analytics solutions. As a result, your digital assets will truly fuel your business growth and help differentiate from competition. Who wouldn’t want that?

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Benefits of Enterprise Data Science

Now that we’re all on the same page about what enterprise data science entails, it’s time to discuss the benefits that it can bring to your organization. In reality, the advantages of this technology can be quite far-reaching, but today we’ll focus on the three main ones that you can definitely look forward to.

Identification of New Opportunities

Identification of New Opportunities

One of the primary benefits that data science can deliver to your organization is the ability to uncover new opportunities. Once you begin analyzing the digital information that you have on hand, you’ll gain access to insights that were invisible to you before.

For example, by centralizing all of your customer data, you’ll be able to identify patterns of behavior that different segments display. Thus, empowering you to develop personalized marketing campaigns that deliver lasting results.

Improved Operational Efficiency

Improved Operational Efficiency

Thanks to the automated pipelines that facilitate information processing, your organization’s data will always be available for analysis. Moreover, with the help of interactive visualization dashboards, you’ll quickly pinpoint the areas of improvement to work on instead of spending hours generating reports and looking for meaning within them.

Find out how we carried out Data Automation for an Insurance Consultant

Decreased Costs

Decreased Costs

Lastly, thanks to higher levels of automation and efficiency, you’ll naturally observe lower costs than prior to the implementation of a coherent data strategy. Plus, due to a better analysis of your expenditures, you’ll be able to identify preferable ways of resource allocation that won’t compromise productivity or performance. Thus, further optimizing your spending.

Common Data Science Pitfalls to Avoid When Starting Out

Enterprise data science isn’t always sunshine and rainbows. In fact, it can get quite challenging, especially when companies focus on short term gains as opposed to long term wins. So, before you dive into your next initiative, take a peek at the common pitfalls that you should be on the lookout for.

Common Data Science Pitfalls to Avoid When Starting Out

Failing to Define Business Value

No matter the type of software development project you’re pursuing, everything starts with identifying the end goal. What are your objectives? How is the undertaking going to bring you business value? You’ve got to answer these questions before starting your data science initiative. Otherwise, you risk losing sight of what’s important whenever it’s time to make prioritization decisions.

So, don’t rush and make sure that all the key stakeholders are on the same page prior to project start. You don’t want to be dealing with unnecessary delays that could’ve been avoided with proper planning.

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Overlooking Bias in Data Sets

Another major pitfall that some enterprises forget about is algorithm bias. You see, data sets can sometimes have prejudiced patterns within them, or data scientists may even unconsciously build in biases into algorithms and models they develop.

As you can imagine, such mistakes can skew analytical results and deliver misguided insights that do more harm than good. Not to mention, these mishaps can also raise serious ethical concerns that significantly damage your firm’s reputation.

Hence, if you’re looking to develop a well-functioning enterprise data science platform, don’t forget to account for potential algorithm bias and triple check your models. It will save you many headaches in the long run.

Underestimating Security Importance

Of course, there can be no data science without data itself. Ideally, large enough amounts for algorithms to train on and be able to extract insights that are actually valuable to your business. However, the more digital information you store and process — the higher the risk of security breaches.

Thus, it’s essential to keep data safety top of mind when starting out development. If your business operates in the healthcare, fintech, or insurance sectors and deals with a lot of personal information, it’s especially important to implement firewalls and use data encryption. After all, you don’t want any leaks to disrupt the operations of your company.

Find out how Blockchain Protected a Startup from Security Breaches

Disregarding Support and Maintenance

Finally, the last pitfall some organizations tend to succumb to is disregarding support and maintenance. You see, for your data science systems to work and continue processing information in the best manner, you’ve got to keep them up to date and optimized. Otherwise, you risk wasting all the money you’ve invested originally and missing out on the benefits we’ve discussed above.

So, whether you choose to have an in-house support team or prefer to outsource to external vendors — just make sure your software is functioning up to the highest standards.

Bookmark our Guide to Addressing Common Software Development Challenges

Begin Making the Most of Your Enterprise Data

Well implemented data science initiatives can truly set your organization apart, help identify new business opportunities, and drive efficiencies across all company levels. However, it’s important not to get caught up in the advantages this technology can deliver and keep in mind the common pitfalls that should be avoided.

At Velvetech, we understand the importance of making the most of your data and developing optimal ways of leveraging it for your business. Thus, we are happy to provide data science services to companies from all kinds of sectors and help them reap the rewards of effective data strategies.

So, if you’re looking to partner with experienced data scientists and software developers — don’t hesitate to reach out. Our team is always ready to discuss a potential collaboration.

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