How to choose a reliable big data enterprise analytics provider?

According to an Accenture study, 79% of enterprise executives say that companies that do not adopt Big Data will fall behind the competition. And more than 80 % of enterprises started using big data to improve their business decisions. They have chosen to invest primarily in such areas as reduced expenses and improved customer service. 

However, according to a survey by McKinsey, not all big data initiatives are successful.

‘When we plotted the performance figures for the 80 companies (exhibit), we found that in a few of them, big data had a sizable impact on profits, exceeding 10 percent. Many had incremental profits of 0 to 5 percent, and a few experienced negative returns

In the same survey, those that had negative results stated as the key reasons for such returns: poor quality of data, lack of specialists, and inability to scale up their big data activities. 

Though there are many Big Data enterprise analytics tools and platforms for different purposes, they all need some level of customization and integration into an enterprise ecosystem. What’s more, you need to have the data appropriately structured and cleaned up. Only then, you can extract real business insights from it. 

So how can you find and choose a competent and reliable big data enterprise analytics provider that will help with that? What criteria should you consider and how to build successful cooperation?

How to choose a reliable enterprise analytics provider

1. Choose the location with the best big data development expertise 

While searching for the best location to find a big data enterprise analytics provider, take into account the number of big data specialists within the country as well as the level of competition for these experts. For example, Germany has over 30,000 professional data scientists and big data engineers, but the number of tech companies fighting for experts is enormous. Therefore, many enterprises and large tech companies look for the provider in Eastern Europe. And they do it for a good reason. 

The number of Data Scientists and Big Data engineers here amounts to more than 150, 000, according to Linkedin, with Poland and Ukraine taking the lead.

2. Consider the tech stack of a potential big data enterprise analytics provider

You need to choose the right experts for the right tasks. For real Big Data outsourcing projects, you need very experienced specialists both proficient in Big Data tech stack and with very strong coding skills (Scala, Python).

Tech stack your big data enterprise analytics provider need to to be proficient at:

• Hadoop ecosystem and Apache Spark. They allow large data storing and processing by distributing the computation on several nodes.

• SQL/NoSQL databases.

• Such big data tools as RedShift, Hive, Athena for querying data.

• Experience in maintaining old MapReduce Java code and rewriting it using a more recent Spark technology.

• From the point of view of programming, the focus is on Scala, Python, and Java.

• Kubernetes constructs that are used to build a Big Data CI/CD pipelines.

• Kafka or Google Cloud Dataflow for big data streaming and processing, etc.

Also, you may need expertise in microservices and big data cloud services. Given the elasticity of the cloud, you will be able to store and work with large volumes of data relatively easily, as well as control the quality of your data.

For example, Gogo, a global inflight connectivity provider, needed to improve their customer experience and move their data solutions to the cloud. The company shaped a long-term partnership with N-iX, a big data analytics provider, to perform a complete transition of Gogo solutions to the cloud and build a unified data platform that collects and aggregates all the data from different sources using Spark.

3. Choose the big data enterprise analytics provider that is able to understand and meet your specific business needs 

Though the benefits of Big Data enterprise analytics are tempting, companies often face difficulty extracting the real value from Big Data initiatives. They face such challenges as the lack of business KPIs. This, in turn, may cause unrealistic estimates and drain budgets. It takes solid technical talent and a clear understanding of how it is going to help your business objectives. This is a task for big data engineers and data scientists. They analyze your business case and help you define viable KPIs and produce realistic estimates.

In each case, you may need different experts and a specific approach. For some business cases, it is better to focus on Big Data engineering and Business Intelligence to produce almost real-time and lucid reports and monitor the profitability of different departments or prevent fraud. Or, you may need to combine Big Data engineering and applying the Data Science/ Machine Learning models to the collected and preprocessed dataset.

4. Find the vendor with long-standing Big Data engineering expertise

However, in all cases, the data is the key to success. The larger the dataset is, and the cleaner the data is, the more accurate the results are. 

Any Big Data Analytics project is about building an orchestrated ecosystem of platforms and tools that collect a great amount of data from dispersed sources, cleaning, aggregating, preprocessing it, in some cases, applying Data Science or Machine Learning models, and visualizing the insights. 

And before applying any algorithms or visualizing it, you need to have the data appropriately structured and cleaned up. Only then, you can further turn that data into insights. In fact, ETL (extracting, transforming, and loading) and further cleaning of the data account for around 80% of any Big Data analytics time.

5. Make sure the company has a proven track record of business intelligence development 

Besides collecting and processing large amounts of data, it’s extremely important to produce intelligible, timely and lucid reports. BI developers must be proficient in using various BI tools, including MS SQL, Oracle, MySQL Hbase, BigSQL Data Lakes, AWS Redshift, SSIS, SSAS, Pentaho, Tableau, QlikView, Power BI, and more.

A reliable big data enterprise analytics provider will help you with the following stages of BI development: 

1. Understanding your needs and your business KPIs. That will involve tech leads and experienced business analysts.

2. Analyzing data sources, building an orchestrated ecosystem of platforms to extract data from different sources.

3. Extracting, transforming and loading processes.

4. Optimizing the performance of business intelligence solutions, for example, when they work with delays and don’t provide timely insights.

Lebara, a global telecom provider has partnered with N-iX to optimize their Business Intelligence development and make the reports more relevant and timely. The reports are sent to end-users of various Lebara’s departments in 5 countries. A sales department uses them to track how the department performs; a financial department - to get a clear picture on what is going on with the client base and whether the business is profitable; a marketing department -  to track the success of marketing campaigns. 

Wrap-up  

Finding a reliable big data enterprise analytics provider is a tall order. The company should be both able to analyze your business needs and have sufficient Big Data engineering expertise to meet them. We’ve compiled some guidelines that will help you find and establish successful cooperation with a potential big data service provider.

Please feel free to contact our Big Data consultants if you have any questions.