Six Dos and Don’ts for Outsourcing Big Data Analytics

“Big Data” is a prevalent term in many fields, but it is a particularly critical concept in software development. Some of the most successful companies in the world rely on big data analytics. Among them are huge companies like Starbucks, Capital One and T-Mobile, all of which have the means and experience to organize teams of data scientists. However, because of the great cost and steep learning curve needed to assemble an in-house team of data scientists, outsourcing big data analytics can be a smart move for many small to mid-sized companies.

Integrating offshore experts into your organization’s big data team is an important step in improving your business value. It might even be the best way for your company to stay ahead of the competition in this rapidly changing space. Continue reading for the dos and don’ts of big data outsourcing.

Do: Give Consultants a Trial Run

While you hired somebody to analyze your big data, that doesn’t necessarily mean that you’re stuck with them forever. You should be mindful of vendors wanting you to take a huge gamble on a new system or requiring you to commit to a yearlong plan.

StubHub CMO Ray Elias advises businesses to “stay away from big data advisors pushing traditional big consulting projects. Find a third party who can integrate with your team and add value to what you’re already pursuing.”

When hiring consultants, find a partner that can assist with your smaller, current projects, and before long, you should start to see noticeable results. This method helps to build trust between you and your consultants, after which you can decide if you want to keep them on for longer-term projects.  

Don’t: Employ Data Aggregators

Gathering big data isn’t easy, but you should ask for more from your outsourcing provider than just data collecting and aggregation. You want the consultants you hire to also be able to explain what all of the data actually means. They should be able to interpret the outcomes and evaluate them for profit and growth analysis. The aggregation of data is only useful if their ability to connect it to solid business results matches up.

Do: Protect Your Data

In other words, open up, but proceed with caution. Because you will be providing access to your data when employing a big data service provider, you should provide secure access either via the cloud or behind your network’s firewall. Keep in mind that you need to be aware of limitations or liabilities if your data ends up in the hands of your competitors or others who shouldn’t have access. What safeguards do you have against loss or other financial burdens? For example, you could simply provide customer information but without identifiers such as specific names.

It’s also key to have an exit strategy in place once the working relationship ends. Spell out specifics about data ownership so you – and only you – retain rights to your data.

Don’t: Outsource the Results

You shouldn’t outsource the entirety of your big data analytics initiative. Along the same lines as protecting your data, when outsourcing a large portion of your big data analysis, organization leaders should maintain complete ownership over the project. You don’t necessarily need to be an expert in big data analysis, but you and your in-house team members should be able to make deductions from the results. You want to have a team that can manage and own the outcomes to best extract customer reactions.

Do: Understand the Alternatives

There are several database options that can now be extended with new features that enable the implementation of big data analytics. PostgreSQL, for example, is a relational database management system that allows users to store data securely. This database server is used prominently among mid-sized data warehouses. As hardware is getting bigger and faster, the definition of a mid-size data warehouse continues to grow and user expectations are rising. The PostgreSQL database is constantly adding new features and better performance for big data in order to stay ahead. The latest version, PostgreSQL 9.5 Alpha 2, not only supports UPSERT and more JSON functionality, among other key capabilities, but it also has some major enhancements for big data.

Another option is mongoDB, a NoSQL database. NoSQL databases provide storage for data that is modeled in ways other than the normalized tables used in relational databases. MongoDB has the ability to support your applications and store and analyze billions of documents to deliver real-time results. Today, mongoDB can replace expensive legacy systems with a standard solution that runs on commodity hardware. Because it’s open source, mongoDB can be implemented much less expensively than standard models. Furthermore, with its schema-less design, users are able to bring in multiple new big data sources without needing to prepare it in the traditional sense.

On the other hand, MySQL is an open-source relational database management system. MySQL is used in many high-profile websites such as Google, Facebook, Twitter and YouTube. This type of database is a popular choice for use in web applications and data management. It is a leading database for web and cloud-based applications, and it is integrated with numerous big data platforms. One way to use MySQL is as a big data store, which will often be sharded (yes, that’s spelled correctly) to support such large volumes of data. It can also be used in conjunction with a more traditional big data system to store vast amounts of data and sent to MySQL for analysis.

Even with their new features, for many companies, the complexity of PostgresSQL or mongoDB or MySQL has been challenging because they lack the in-house skills to scale them. Hiring a third party greatly increases time-to-value for big data initiatives because organizations need to acquire the necessary talent for implementing these complex database technologies.

Don’t: Neglect the Analysis

Much of the results from big data analytics can be fairly technical. Nonetheless, you should work to comprehend the outcomes as best as you can. Consider putting together your own internal team to work on the analysis. All in all, don’t forget that strategic decisions are within your judgment and authority and shouldn’t be delegated to a hired hand, no matter how capable they might be.

Ultimately, when hiring an outsourcing partner to help you with your big data needs, be thorough and methodical. Analyzing big data can definitely be a winning factor for business, but only if it’s done right. Managing big data is important in tackling business problems and improving a business’ value overall. However, the cost of those skills is too much for many enterprises—the trick is to find the right partners and work with them effectively.

Contact Accelerance to connect with one of our world-class big data analysis partners through our free rapid referral process.