Big Data analytics and operational strategy

Big Data analytics and operational strategy

Big Data analytics and operational strategy

By Saju Sankaran Kutty

The future belongs to companies that can out operate their competition. This focus on operations requires understanding the impact of the business processes, response of the customers, the changing needs of the customer base and evolving products and services to meet this demand. As the speed of innovation increases and the time to react decreases, it requires companies to understand signals that impacts the business. Understand its impact, the risk and react in real time. This phenomenon that has led to the explosion of the volume and variety of data to be processed, and also the speed at which it needs to be processed in real time. Big Data Analytics fills this need.

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The benefits from leveraging data and analytics on operations are:
1. Customer Intimacy
2. New products and Services
3. Operational Efficiencies

In a digital world with most of our interactions being captured in the form of transactions or interactions there is potential to build a digital persona based on this trail of data. In this world you need to understand your customer intimately and customize your offerings to meet individual needs. There are numerous challenges primarily because of the silos of interaction channels that exist in an organization and un-curated open sources like social media.

One of the byproducts of understanding the customer knows their needs and their interaction with your products. This knowledge base helps creation of new products and services enabling new revenue streams. Operational efficiencies can be achieved through productivity improvement cost and risk reduction in operations. This is not just a bottom-line driven for e.g. some supply chain optimization functions in retail like stock outages can have a direct impact on the top line. In these cases, predicting these risks and failures and mitigating them becomes a game changer.

There are many opportunities to improve productivity and profit gains however the question for most companies is HOW?

The repeated questions that I have heard are
– We have invested millions on building the current BI landscape do we need to redo this?
– The proposition of bringing all the data together using big data is compelling but do you understand the cost involved?
– How do we fund this initiative? Build it and they will come or from a use case focused approach?
– Do you get phenomenal breakthroughs that can alter your business and increase sales?
These are very valid concerns and the answers to these may not agree with the hype that we see in the industry.

The promise of Big Data analytics: Leveraging all your data

Improving operations demands a complete view of the enterprise. Data here reflects the behavior of the system and we need to look at all data generated. This creates two needs 1.) Collect data across all the silos of operation and organization 2.) Leverage Big Data capabilities that can handle large data volumes and variety. This data needs to be made available through a strong security framework to enable the organization to make data driven decisions. This is what we call an Analytics driven organization.

The challenge here is breaking down the silos of data. This has been attempted earlier through data lakes and data aggregation mechanisms. But this aggregation into one data lake or one location is difficult from a technical regulatory, governance and legal perspective which is true of large distributed enterprises.
If the problem is about aggregating the data, we have been doing this with data warehouses. So what is the difference?

Data warehouses were built on the data supply chain approach. It presumed a demand that was being supplied. In an analytics driven organization we do not have this demand driven and so we have to make all data in the system available. This means that we need to capture data in its raw form from OLTP systems and defer the interpretation of the data to the points of consumption least we miss the latent signals in the data. In addition, we have real time streaming data that has external and internal events that are a result of the organization’s actions and the changes environment. The response to these events gives an idea about the behavior of the system.

The response from most enterprises today is to create a big data platform that combines all the disparate data. The ability to learn from the actions and reactions to events makes this platform an intelligent entity that interacts with the customer.

Delivering value to your operations

Integrated platforms that bring all data together will help to accelerate the value delivered to operations.

Customer Intimacy

Understanding customer interactions and transactions helps customer identification and classification based on their behavior. It is about identifying what makes the customer unique much like a customer genome. We have been investing in building such solutions that have relevance for retail companies to target individual customers and create individual assortments to increase revenue and also for other industries

New Product and Services

Leveraging existing data, new data and events gives insights to improve existing and new products and services.

In many cases post facto analysis is not an option or very expensive. A mining company operating in remote areas uses large machines and analyses the real time feeds to predict problems before hand. This is used to reduce the downtime due to machine breakdown. A similar pattern is being used by number of manufacturing companies that are turning it into customer service offerings generating revenue.

Operating Efficiency

Understanding the inefficiencies in your supply chain from a cost and efficiency perspective will lead to significant value creation.

Solutions like Augmented Data Warehousing Leveraging Big Data technologies have been used to reduce the cost of BI solutions. Supply chain visibility solutions with an instrumented supply chain can find inefficiencies in your processes making significant difference to businesses. We worked with one of the apparel retailers and identified that managing inventory by optimising across stores would help to increase the profitability by 3-5 percent.

Big Data Analytics can detect trends and correlations creating significant value.

How and where do we start?

Most organizations start with experimenting on business initiatives on customer intimacy or customer service.
To turn this into data and analytics driven culture you will need to:

1. Start with a plan on leveraging Big Data and Analytics across the organization.

2. Take an augmentation based approach by leveraging the existing investments in BI and build a platform that makes your data available across the organization. In our case we call this the boundary-less landscape.

3. Define the funding model that can help you realize this vision. This is a critical step without which this initiative will not be sustainable. You will need to determine at this point if you want to choose between a federated, centralized or hybrid model for your analytical organization.

4. Focus on the customer and realize this plan by delivering business value with each initiative. I see a lot of customers who take a “build it and they will come” approach. Without a particular constituent who can deliver business value from this approach I have found this run into rough weather.

5. Remember that to effect operations it is not data that matters. It is an enabler. It is not insights that matter it is knowledge. What matters is actionable insights and it is the actions that will deliver value. Create the solutions that will help you make the insights operational very quickly.

Finally, Big Data analytics has the potential to deliver significant impact on operation strategy and deliver business value. Build your systems for incremental insights around known areas of inefficiency and making the process of data exploration easier. It will occasionally lead to break through insights and fundamentally business altering ideas. You need to pay particular attention to the total cost of ownership or price point of your solution and constantly innovate to match this against the value that is delivered. In most cases the biggest value will be around the Analytics driven culture that will be created in the organization.

Source By tech.firstpost…



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