implementing big dataThe conversation has shifted from trying to define what Big Data is but we’re stuck in the layers. Is Big Data fun like eating an orange full of vitamin c? Or is it more like an onion? where you cry when you try to peel it off but contrary it’s so sweet when you cook it properly? I tend to lean more towards an orange, and I will tell you why.

One important area is the learning curve. We are seeing efforts taking six months+ without even having addressed any of the use cases let alone having to figure them out. There is a lot of innovation at the moment but with that comes a lot of new terminologies that also require some effort to be informed. More important is the use case. How are we going to evaluate what we need without knowing what the use cases are? A real-time data streaming scenario is much different than scenarios where the speed is not of importance as we can afford to see the results the next day.

Three Steps to get started with Big Data

  1. Start by identifying your vertical and finding what the demands are and who you serve as your customer. This will guide you on how to decide the Proof-of-Concept PoC(s) and therefore the use case(s). As a follow-up step, we then look at the technological assets that we will need. From there the next steps is how to launch the Proof-of-Concept (PoC) quickly. For companies that think their data is sensitive follow this rule:  decide what data you can live with by having it in the cloud as you’re only testing the hypothesis at this stage and is the fastest route.
  1. Seamless integration. At this stage, you need a solution that is responsive. Why because multiple business units will come with requirements. You need to be able to accommodate such demands. I can’t stress enough the managed services approach until you can support it internally. Why because it’s a huge learning curve and the return on investment (ROI) is far better. Many fall into the loop that because they see what a super enterprise is doing, then it’s feasible to have the same approach. This approach will only yield frustration as it will take longer. At Orzota BigForce we’re working hard to accelerate this stage.
  1. Insights. At the end of the day, you are doing all this for the insights but more specifically for the predictive and the hidden potential. Most likely you have a ton of reporting going on. Reports it’s not the issue here. If you cannot get the proper reports right now, then you need some serious help. Insights can be discovered internally but also externally, so kindly remind your hardest critics that is not about how much data you have but how much is out there that you can derive these very crucial insights for your business. A good example to put this argument to bed is Social listening and therefore Sentiment Analysis.

In conclusion, if you think about it, there isn’t much overlap if you start from the use case and Proof-of-Concept (PoC) approach while following the above steps. Starting small will also allow you to get buy-in and then expand. Partners may seem in the beginning that they offer similar services but at the pole position, you need to get more with less. Lastly, always keep in mind that for the majority the dynamics are different as there isn’t much Data Analysis talent out there thus with a managed services approach or a hybrid approach you can accelerate your environment and team. Finally, the orange correlation in 3 easy steps: pick, peel and consume. You can enjoy it getting all the vitamin C and its benefits.

big data analysis

Does your Analytics journey look like this?

analytic solutionsBig Data and Analytics is now moving at a faster pace than before to midsized businesses (MSBs). The potential “Value” of Analytics while once thought useful to larger global enterprises, has quickly moved downstream. As a consequence, the spend in this space is also expected to grow faster than the large-scale enterprises.

Midsize businesses across domains can now gain a significant competitive advantage, get valuable insights to identify potential markets, and form the basis to improve customer experience and operational efficiency.

However, there’s more money spent on efforts to cope with massive influx of available data than the applicability of “Analytical Value” that technology offers. To influence business results meaningfully, MBs must take a multi-dimensional view of the Analytics Value. They must consider the Value across the following three dimensions:

Intent, Commitment, and Clarity of Business Impact

It is imperative to understand the purpose and focus areas where Big data and Analytics have the most potential. Here are few key questions:

  • What do we want to use big data for? Strategic? Tactical?
  • How can we monetize the data streams in terms of customer loyalty, revenue growth and/or cost reduction?
  • What are the areas for Business Impact (Customer, Product/Service, Operations, Supplier/Partner, Finance, Risk)?
  • Where within these areas does Big Data and Analytics provide the most sustainable value?
  • How can we use data-driven customer intelligence to understand customer behavior?
  • What specific customer-centric and operational-centric KPIs or metrics provide insights into a particular component of our business? e.g.: Propensity to Buy, Customer Lifetime Value (CLV)
  • How will better insights and information help overcome the most pressing challenges in our business?

Solution Options Built on a Foundation of Analytics

Most medium sized businesses lack understanding of the various Solution Options and Tools available, and hence are not confident and hesitate to employ it. It is imperative to select diligently from a plethora of Analytics Solutions and Tools for cost efficiencies, process improvements, data governance, and technology.

From a process perspective, they must be able to collect enough internal data, normalize and combine this data with external data sources to identify patterns and behaviors.

A study by IDC revealed that organizations that use diverse data sources, analytical tools (e.g., predictive analytics) and the right set of metrics are five times more likely to succeed and exceed expectations for their projects than those who don’t use these big data strategies.

Skills Training, Gap Analysis, and Lessons Learned

Having a successful initial implementation or Proof Of Concept (more is better, timing is of essence) within 6-8 months is critical. Shorten decision paths and leveraging domain centric Big data and Analytics partners and solution providers is essential.

However, identifying and realizing Analytics Value for midsize businesses is also more than just working with external partners. They must know the critical questions to answer based on the data.The business must understand the Analytics terminology and technology, as well as possess some internal statistical knowledge. We see a few midsized businesses include a new role – a Data Strategist, to help with their growth strategies, streamline business operations and integrate technology to help the business operate more efficiently.

Please feel free to reach out to learn how Orzota has helped organizations across the above dimensions to:

  • Build models targeted to specific use-cases that can be implemented swiftly, with clear business focus
  • Select, deploy of targeted data-analytic solutions
  • Adopt the Analytical solutions and tools
  • Realize Analytics Value Faster