The data value chain

The terms “Data driven” and “Big Data” are the buzz words of today, hyped definitely, but the implications and potential are real and huge! Tapping into the enormous amount of data and associating this data from multiple sources creates a data chain, proving valueable for any organisation. Creating a data value chain consists of four parts: collection, storage, analysis, and implementation. With data storage getting cheaper, the volume and variety of data available to be exploited is increasing exponentially. But unless businesses ask the right questions and better understand the value that the data brings in and be sufficiently informed to make the right decisions, it does not help storing the data. For example, in marketing, organisations can gather data from multiple sources about acquiring a customer, about the customer’s purchasing behaviour, customer feedback on different social media, about the company’s inventory and logistics of product delivery. Analyzing this stored data can lead to substantial number of customers being retained.

A few of the actionable insights can be as follows:

Improving SEO (search engine optimization), increasing the visibility of the product site and attracting more customers
CRO (Conversion rate optimization) i.e. converting prospects into sales, by analzying the sales funnel. A typical sales funnel is Home page > search results page > product page > proposal generation and delivery > negotiation > checkout
Better inventory control systems, resulting in faster deliveries
Predicting products that a consumer might be interested in, from the vast inventory, by implementing good recommendation algorithms that scan through the consumer behaviour and can predict their preferences
If some of the above points are taken care of, customer loyalty can increase manifold, based on the overall experience during the entire consumer lifecycle.

Often the focus lies on the Big data technology rather than the business value of implementing big data projects. Data is revolutionising the way we do business. Organisations, today, are inundated with data. To be able to make sense of the data and create a value chain, there has to be starting point and the customer is a good starting point. The customer’s lifecycle with experiences at every touch point defines business growth, innovation and product development. The big data implementations allow blending data from multiple sources leading to a holistic single view of customer, which in turn gives rise to enlightening insights. The data pretaining to customer, from multiple sources, like CRM/ERP/Order Management/Logitics/Social/cookie trackers/Click traffic etc., should be stored, blended and analysed to gain useful actionable insights.

In order to be able to store the gigantic amount of data, organisations have to invest in robust big data technologies. The earlier BI technologies that we had do not support the new forms of data sources such as unstructured data and the huge volumes, variety & velocity of data. The big data architecture consists of the integration from the data sources, the data storage layer, the data processing layer where data exploration can be performed and/or topped with a data visualization layer. Both structured and unstructured data from various sources can be ingested into the big data platform, using Apache Sqoop or Apache Flume, real-time interactive analyses can be performed on massive data sets stored in HDFS or HBase using SQL with Impala, HIVE or using statistical programming language such as R. There are very good visualization tools, such as Pentaho, Datameer, Jaspersoft that can be integrated into the Hadoop ecosystem to get visual insights. Organisations can offload expensive datawarehouses to low cost and high storage enterprise big data technology.

bigdatarch — Edited image from Hortonworks

Irrespective of the technical implementation, business metrics such as increasing revenue, reducing operational costs and improving customer experience, should always be kept in mind. The manner in which the data is analyzed could create new business opportunites and transform businesses. Data is an asset and investing in a value chain, from gathering to analyzing, implementing, analyzing the implementations and evolving continuously, will result in huge business gains.

Streamlining the process of processing

The customer expectations are very different, now. Decisions need to be taken in real time, to convert a prospective customer into committing. In an age, where customer seeks instant gratification, organisations that have a longer time-to-market due to cumbersome internal processes, customer loyalty is hard to win. For example, a customer visits your physical store, if you offer a discount at the very first visit, the chances that the customer will revisit your store are high. On the other hand, if you are merely noting customer behaviour which then has to pass through unwieldy processes, later, to mete out a discount coupon, the second time the customer visits your store… if at all, is a thing of the past. The advanced analytics systems now, are able to handle data influx from multiple disparate systems, cleanse and house in the dmp (data management platforms), ready to be queried in real time to cater to predictive and actionable insights, on the fly.

However, if the business methodologies used are not complimenting this speed of data processing, the business will still suffer. The widely used, Lean methodology preaches creating more value for customers with fewer resources. Anything that does not yield value should be eliminated. But organisations need to adapt to only the best of the best practices. Following methodologies by the book, on the contrary, causes bottlenecks. To be able to leverage more out of the Business Analytics systems and solutions, the processes and tools, both, need to be streamlined to create customer satisfaction. A lot of the business intelligence projects take too long to deliver and are inflexible, resulting in the functional business teams procuring BI tools which promise quick wins. The problem with such data discovery tools, apart from creating data silos, are that they lack data governance, hinder data sharing at an enterprise level and increase licensing costs.

It is not a solution to have no business process at all. There needs to be accountability and that comes from business processes. It is a continuous iterative process to find the right balance between processes and the speed of delivering value to keep the costs low and increase the profitability of any business. One size does not fit all and it applies to organisations, as well. Methodologies/processes need to be tweaked, tuned and tailor made for each company. Organisations that try to implement Lean/Agile/Scrum but fail are because they lose the customer focus, some companies do not have a clear strategy in place with employees being assigned foggy responsibilities and lack of communication and this in turn results in the focus shifting from the task at hand to the nitty gritties of such project management methods.

To avoid pitfalls, a clear business strategy needs to be defined specifying business goals in order to maximise gains. The next step is to trim all the processes that lead to this gain.

The bridge between Business and Analytics – Business Data Analyst

The terms business analysis and data analysis have traditionally seemed different. With the increasing amount of data available, stored and the need to analyse that data and gain business insights out of it, a new role, Business Data Analyst is critical. Companies lacking the business data analysis talent pool have a lower ROI and will lose out to companies hiring analytics talent.

Most companies, even today, have the two competencies separate. Business analysts analyze functional requirements and help translate the same to technical specifications while data analysts are more technical, gathering, cleansing and analyzing data. To increase the analytic throughput of a company it is vital to combine the business and analytic competencies to be able to analyze the data from a business aspect, being able to draw conclusions about consumer behaviour, find trends and accordingly make business decisions with targeted marketing campaigns.

As this is an emerging field, it can be challenging to find right people with both the business acumen as well as analytics skillset. There can be myriad ways to bridge this gap. One strategy can be to create teams of people with direct marketing roles along with data analysts and data scientists to utilise the combined specialised competencies. Another strategy can be to train the management team’s analytical skills or beefing up the business knowledge of data analysts.

No matter which strategies are adapted, the new role of Business Data Analyst is paramount for enabling a company to make the right investments at the right time to yield an ROI. Building a data driven company is more than identifying the right BI tools, it’s about driving business through customer behaviour feedback by analyzing data.