Fast and furious

People don't just need fast data, which is really real-time data by another name but infers the data or information derived, received or consumed needs to be relevant and actionable: it must, for example, initiate or enforce a set of follow-up or completion tasks.

Fast data is the result of data and information throughput at high speed. Real-time data has always been an enabler for real-time action that allows companies to respond to customer, business and other operational situations and challenges - almost immediately.

Fast, actionable data is that which is handed to decision-makers or users at lightning speed. But, it is the application of knowledge gleaned from the data that is paramount. Give your businesspeople piles of irrelevant data at light speed, and they will only get bogged down. Data consumers need the right insights, at the right time, to effectively marshal resources to meet demands.

The problem for some companies is they are still grappling with big data. There are many more sources of data, there are more types of data, and many companies are struggling to connect the data from beyond their private domains with data inside their domains. However, big data fuels fast data, but it must do so in real-time, after being clearly interpreted and prepared so decision-makers can take action. And it must all lead back to improving customer service.

Why focus on customer service? Because, as Roxana Strohmenger, director of Data Insights Innovation at Forrester Research, says in a guest blog: "Bad customer experiences are financially damaging to a company. The damage goes beyond immediate wallet share to include loyalty, which has potentially significant long-term financial implications.

"Retailers, for example, are using the Internet of things (IOT) to improve customer service. That's essentially big data massaged and served directly to customers. The International Data Corporation (IDC) 2014 US Services Consumer Survey found 34% of respondents said they use social media for customer support more than once a month. Customer support personnel, who cannot access customer data quickly, cannot efficiently help those people. In a 2014 report, Forrester states: "Companies struggle to deliver reproducible, effective and personalised customer service that meets customer expectations."

The concern for many companies is they don't get it right in time to keep up with their competition. They could spend years trying to regain market share, at enormous expense.

So, fast data can help, but how is this achieved? In reality, it differs little from any previous data programme that feeds the business decision-makers. The need has always been for reliable data, available as soon as possible, which helps people to make informed decisions. Today is the customer era. The advent of digital consumer technologies have given consumers a strong voice, with the associated ability to hold widespread sway over company image, brand perceptions, and other consumers' product choices. They can effectively influence loyalty and wallet share, so their needs must be met properly and quickly. Companies need to know what these people think so they can determine what they want and how to give it to them.

Big data fuels fast data.

All of this comes back to working with data. Data warehouses provision information to create business insight. Business intelligence (BI), using a defined BI vision, supporting framework and strategy, delivers the insights that companies seek. Larger companies have numerous databases, data stores, repositories - call them what you will, their data sits in different places, often in different technologies. Decision-makers need to have a reliable view into all of it to get a consistent, single view of customers, or risk erroneous decisions.

Data warehousing, BI and integration must be achieved in a strategic framework that leads back to the business goals, in this case, at least partly, being improved customer service to make it cost-effective, efficient, effective and deliver proper return on investment (ROI).

The following standard system development life cycle process also applies to the world of immediacy, driven by digital technologies:

1. Audit what exists and fix what is broken
2. Assess readiness and implement a roadmap to the desired outcomes
3. Discovery - scope requirements and what resources are available to meet them
4. Design the system - develop it or refine what exists
5. Implement the system - develop, test and deploy
6. Train - executives and administrators
7. Project manage - business users must be involved from the beginning to improve ROI and aid adoption
8. Maintain - this essentially maintains ROI.

Fast data relies on task and delivery agility using these pillars, which are in fact age-old data disciplines that must be brought to bear in a world where there are new and larger sources of data. The trick is to work correctly with these new sources, employ proven methodologies, and roll these out for maximum effect for customer satisfaction.