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

5V of big data

Since the explosion of Big Data we have early adopters and also a fan-base that is still in the evaluation phase. Everyone is familiar with the 4 V’s in Big Data: Volume, Variety, Veracity and Velocity. These are fantastic fundamentals but we need a 5v of big data.

Introducing the 5V of Big Data

vision  It seems that many are stuck in the definition stage and endless discussions that don’t lead anywhere. There is a plethora of innovation and all kinds of companies that are providing a solution to the many moving parts of Big Data. In an earlier post, I wrote about the three pillars of Big Data: Modernization of Data Warehouse, Data Plenum and Data Juncture, Insights.  All good to this point but…

Why Do We Need the 5th V (vision) in Big Data?

  • Vision help us to identify where to start.

We have to envision the complexity and ultimately where we need to take this program. And yes folks, it is a program. By spending a minute to look into the next few months, we can identify at least internally at this stage an initial framework of action items. You have to remember that depending on the size of your company, you have to be a team player on this.

  • We can get a better understanding of the playing field.

Big Data has complexity, and this is why it’s so much fun to work in this domain. Your Vision should be centric to the goals of your company as a whole! This means that it should serve the needs of your internal and external business efforts. Let’s take an example for a B2C company. If you provide products/offerings to consumers, you will have different needs that are around how to understand your customers better, how they shop online vs. store, marketing, and predictive insights. When you are envisioning, take a minute to understand how you will get there by servicing the needs of the business requirements.

  • Building the program

One of the final elements of the Vision is to initiate the program. You have to remember that it is going to be an effort that will require external assistance and internal coordination. A major accomplishment in this area is to quickly be able to accommodate the business requirements thus providing big data driven solutions to the company users. I can’t stress enough the importance of a managed services model and here is why: first, you eliminate the complexity and second, you can deliver faster to the business users. Also, you have to remember that there is a need for nurturing and continuous modeling. Finally the team structure. In team sports, we often hear the word franchise player the person(s) that the team is built around. Whether you like the NFL or the NBA you can’t go anywhere without a good quarterback and in basketball point guard. Then you build from there wide receivers, center you get the point.

In conclusion, Vision, the 5V of Big Data, would be a catalyst to create initiate the steps that get you to successfully build the process for Volume, Velocity, Variety, and Veracity. Remember that we are all Big Data analysts and that analytics in one way or another are ingrained in our human system. From a young age, we played sports, so we looked at stats and knew what to do or we knew how much time to spend to improve our game, therefore same for business. One last point though it takes a team to win the championship keep that in mind! No need to feel that we’re drowning or intimidated by data and likely there is plenty of innovation out there at the moment. So take a minute to use the 5th V (Vision) and keep moving forward to derive value from Big Data.

awesome-elephant-and-little-girl-with-violin

It’s an exciting time for business. Companies have either amassed a tremendous amount of data or are increasing their efforts to ingest data available from this digital ecosystem that we’re part of to find value and new profit streams. No matter how we look at it there is a need to make sense out of all this.

Time to move from spending precious time for defining Big Data and actually taking steps of doing something with the data available. Before we do this though we need vision. On an earlier post I added the 5th V for Big Data Vision. This is an integral component to be on the right path of profitability. Enter the 5 ways to getting to insights fast.

  1. Modernize data access

More and more as we engage on this strategy there is going to be a need for data engineering. Perhaps one of the most important layers. Unfortunately there are many moving parts in Big Data and being an open source technological framework there is a high curve of learning. A good practice is to do an assessment so you have the roadmap blueprinted.

  1. Data Management & data processing

This step depends on such factors as organizational structure, capabilities to ingest and process a variety of data with velocity. Chances are that in order to be efficient and to accommodate the business demands you have to move swiftly. As such you’ll need solutions. A good model is to look for managed services. It is important that you alleviate all stakeholders as much as possible.

  1. Predictive insights

A managed services model per above will allow you to focus on how to improve the business. A lot of companies today are focusing on the reporting. This is all good but elaborated spreadsheets don’t allow you to have time to explore where the hidden revenue streams are. Time to put the thinking cap on. The paradox here is the human capability.  Predictive modeling is where the action should be focused. A good rule here is that this is a continuous process so don’t treat this steps as one and done.

  1. Problem solving selection

Big Data methodology can help you solve many problems. Improve product recommendations to clients, adjust content creation based on listening ingestion from social feeds, prevention in maintenance or in fighting diseases or customize customer experience, etc. If you’re starting you need to test the waters. At this point you have heard the terminology POC (Proof of Concept) and Use Case (the larger version of the problem you have to solve).  A good rule start with a modest problem that can lead to the full use case solution.

  1. Anticipate Scalability

Revert back to the vision. You will grow and not all companies have the same capabilities. Your historical data will grow, the demand from the business units will grow, and your team will grow. At this stage what you have to keep in mind is that Big Data should be tailored to the exact needs of your organization. Stay away from one size fits all models. Choose nimble technology partners because there is a learning curve and more importantly a high degree of customization.

In conclusion there is a dependency to external partners and also a high degree for teamwork. You have to remember and constantly evaluate if at the end of the day are my problems Big Data problems and that you shouldn’t do Big Data because it’s fashionable but because you have a need to extract value for your business. On my next post I would be writing about the 5th V in Big Data stay tuned.