Top 3 Tips for implementing big data analytics in your business

DeGroote’s Ali Reza Montazemi spoke on the subject of “Big Data Analytics in Support of Evidenced-Based Management” during a recent Knowledge @ DeGroote cocktail event at The National Club in Toronto. The following article is a takeaway from his presentation:

What is digital transformation? It’s the act of changing your organization through technology. It has many different pieces — one of them is collecting big unstructured data, which is called “big data analytics.” Organizations can use big data analytics to help make evidence-based decisions and refine their business processes. Big data analytics present novel ways to approach business strategy through the ability to offer new information, insights, and action. As organizations develop the capacity to implement big data analytics solutions, they will encounter obstacles as well as opportunities. Here are my Top 3 Tips on how to go about enacting big data analytics into an organization.

1. Extending the business strategy toolbox

Big data analytics should be linked to the organizational strategy. In order for organizations to implement big data analytics successfully, they need to create information and decision processes that will allow for real-time execution. In order to execute this, the organizations need the people and talent who are skilled in digital tools and who work effectively in teams. Organizations can use cross-functional teams that are in constant contact in decision spheres to respond to real-time big data analytics inputs such as customer sentiments and requirements. For example, Whirlpool leverages big data when they embed sensors in their products to track actual product usage. They mine social media for customer sentiment, and gather customer-produced content (e.g., customers will post videos of themselves using a company product to the product Facebook page) to understand customer preferences and behaviour. Armed with new data, Whirlpool can advance by generating insight into the needs of its customers for product and services.

2. Integrated enterprise systems

Enterprise system applications and technologies are becoming quite advanced. They can speed up activities, provide intelligent and autonomous decision-making processes, and enable collaborations and distributed operations. Having an integrated enterprise system is critical to successful use and implementation of big data analytics. Enterprise systems can provide digital options that help organizations adapt to:

• Changing requirements more quickly by changing information-based value propositions. For example, 7-Eleven Japan owns and manages a chain of small convenience stores focused on selling a variety of fresh and prepared foods. Stores order and receive deliveries of fresh foods three times per day, with each store’s clerk responsible for ordering the fresh food. 7-Eleven Japan invested in big data (e.g., detailed sales and customer records from its integrated enterprise system, and other sources of data like weather trends) and placed these metrics and trends into the hands of its store clerks using intuitive dashboard technology. This data has given the store clerks a handle on the preferences of its customers: Each year, 70 per cent of all the products sold are new products to the chain as a whole, and 7-Eleven Japan is the country’s most profitable retailer.
• Forging value-chain collaborations with partners. For example, when customers make a purchase from anywhere in the world using eBay’s online auctions, the firm’s sales process integrates with a variety of partner processes that include payment processes (e.g., PayPal), shipping processes (e.g., FedEx), and other partners internal processes (e.g., online retailers who sell through eBay).
• Rapidly exploiting market niches. For example, eBay customers are its de facto product development team because they post an average of 10,000 messages each week to share tips, point out glitches, and lobby for change.

3. IT governance

A lot of value comes from combining data from different sources — both inside and outside a company. However, more often than not, resistance to sharing and combining this data often arises. Organizations need to give power to their digital experts so they can generate new insights from big data. This shift in power is necessary to accomplish the changes that are needed to fully embed the big data analytics capability. IT governance is responsible for overseeing this change. As part of IT governance, top managers are regarded as coaches and champions. It’s their responsibility to create and maintain “change readiness” by using a proactive approach to help teach employees the value of a big data business process. It’s important to note that organizations differ in their capacities to apply, integrate, build, and reconfigure IT resources concurrently with structure (organizational business process) and people. Application of big data analytics must be in line with the organizational IT capabilities in order to succeed.

Professor Montazemi specializes in the design and development of information systems in support of business process effectiveness and efficiency. His research and consulting include business application of artificial intelligence, decision support system modelling and assessment, business process design through information technology, human-computer interaction, and intelligent agents in support of e-commerce.