Big Data, Artificial Intelligence

Contino, the global enterprise DevOps and cloud transformation consultancy, is pleased to announce the launch of its new Data and Analytics practice in the Americas region. Joining us is Manish Shukla, who will be leading the practice, and I recently sat down with him in our New York office to get his thoughts on the rise of data and analytics and the challenges surrounding it.

Why is Contino launching a data and analytics practice in the US?

Enterprises need to operate in a data-driven way in order for them to truly benefit from the digital revolution phenomena. This requires real transformation across Culture, Leadership, Teams, Talent, Infrastructure and Technology.

Contino has helped some of the world’s largest brands to build customer-focused data strategies and cloud-native analytics platforms and it’s only natural that we offer this service to our US-based clients as well.

For example, until recently Jetstar was relying on a traditional data warehouse for its analytics, insights and decision making. By deploying a data lake platform, Contino has enabled Jetstar to handle the volume, variety and velocity of its valuable data sources. For the full story, visit here.

Where does the need for a data practice come from?

The digital revolution has given way to a massive data explosion. Across the board, companies now have more data than they know what to do with.

These companies are faced with the challenge of tapping into a vast amount of previously untapped enterprise data which sits across traditional systems like CRMs and Data Warehouses.

There is so much business value to be unlocked in this data. Whether that’s understanding how effective your marketing campaigns are in conveying your message, knowing if your sales pitches are hitting the sweet spot or simply whether or not your product meets customer expectations — all of this is a goldmine of business knowledge provisioned by Cloud & DevOps Enabled Data, Machine Learning and AI services and products.

What are some of the main benefits of using big data?

Throughout my career, I have noticed some key differentiators between companies using their data the right way and those who are not using it.

If you know how to use your data, this can bring about numerous benefits from improved efficiency and productivity in the workplace to faster and more effective decision-making. I have also witnessed the potential of data to improve a company’s financial performance through the identification and creation of new revenue streams. When used correctly, your data also has the power to improve customer experience and as well as customer acquisition and retention. All of which gives you a major competitive advantage.

There is no question, big data offers big benefits.

What are some enterprise use cases for big data?

Big data is a hot topic right across the business landscape. Leading companies in Retail and Finance are leveraging powerful insights to maintain a competitive edge within their respective industries.

For example, in Retail and Consumer Products, big data is being used to understand consumer needs and behaviors on a much deeper level. Market basket analysis, price optimization, inventory forecasting and optimization and call center analytics are all great ways that big data is being used to deliver real value.

Finance is another industry that has embraced big data. In a sector that places so much importance on risk, data can bring huge rewards. Machine Learning, for example, has proved extremely useful in identifying and dealing with risks — a good example would be the use of ML powered automated advisory services. Other risk-based use cases include AI backed underwriting models, which are used to help customers identify potential risks prior to default. Data has also proved vital in fraud detection when used to identify transaction anomalies or suspicious activities.

While the applications of this data will vary by industry, its ability to unlock business value is consistent across the board.

What are the 4 Vs of big data?

Big data is often defined by the ‘4 Vs’. These four principles shed some light on the challenges surrounding big data practices.

1) Volume: The scale of data being generated

  • We are creating 2-3 quintillion bytes of data every day.

2) Velocity: The unbelievable speed of data

  • Every 60 seconds, 72 hours worth of footage is uploaded to Youtube, 500,000 posts are put up on Facebook and Twitter and 200,000 emails are being sent.

3) Variety: The diversity of data

  • Data comes in so many different formats including documents, videos, photos and relational databases – some of which are structured and some of which are unstructured.

4) Veracity: The accuracy of data

  • Poor data quality costs US companies an estimated $3 trillion every year.

What are some of the challenges surrounding big data in the enterprise?

Enterprises that continue to take a traditional operational approach are not positioned to transition, transform and scale around the ‘Big Data 4V principles’.

In the absence of a data-driven approach, enterprises fail to understand how their untapped data can be harnessed to increase revenue, improve efficiency and productivity and enhance customer experience. These organizations typically lack a clear data strategy on the what, why and how of data and suffer from a very poor process of identifying any gaps in their data to unlock value.

In such enterprises, different departments and teams traditionally operate in silos. This is a huge problem since big data and cloud require cross functional Agile teams and a ‘polyglot’ kind of workforce in order to benefit from data-driven, cloud native DevOps enabled services. To make matters worse, there is a severe skills shortage in data, DevOps and cloud which hinders enterprises from making serious headway on big data.

The sheer volume of data presents huge challenges to these enterprises. Conventional technologies or platforms like data warehouses, BI tools and SAP were never designed or created with the intention of handling such high data volumes, velocity or variety. Cost is of course another factor since it is really expensive to support such high volumes.

How important is the cloud for big data?

Traditional data centers offer little to no flexibility, scalability and cost-efficiency with regards to storing big data. That’s why companies who are serious about big data are moving to the cloud.

The cloud is a natural companion for Big Data with its IaaS, PaaS and SaaS model, cheap storage, unlimited scale and remarkable AI, Data and Analytics services.

With everything in a central data platform in the cloud, you get all the benefits of putting your disparate data sets together — they can run in parallel, they can be queried against each other and they can be started, stopped or scaled at the push of a button. That’s not an option with on-prem.

To find out how Contino can help you to unlock the business value in your data, contact Manish.

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  • Matt Farmer

    Co-Founder and CEO

    Matt is responsible for the successful growth of the business globally, whilst building and leading a talented executive leadership team. Matt brings over 15 years of management and line of business experience from his prior roles where he founded and successfully grew many consulting businesses within the technology sector across development, collaboration and DevOps.

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