Business Intelligence – Always intelligent decisions?

In order to survive business need to know there customers. This goes all the way back to the local shopkeeper who not only the knew the regular shopping basket of their customers, but the status of their marriage and how many drinks they treated themselves to on the weekend.

The same is was true today as it was then only know businesses are faceless corporations who focus on building up a workforce of faceless replaceable employees, who interact as briefly as possible with customers to move more of them through the shop faster. Now instead of building up individual relationships with customers and suppliers businesses can understand their customers through the accusation of data.

In an age where more and more transactions are carried out digitally we are not only exchanging money for goods and services but data along with it. We are divulging shopping habits, location data, gender race and sexuality data every day and allowing businesses to build up profiles that give them a far greater insight into the behaviour of customers than the humble busy body shopkeep could ever have imagined.

In essence in order to make use of this data businesses must examine it and turn it from figures and words into useable information. From this information they gain knowledge that can be used to allow them to gain a competitive edge in the business world.

One example of the clever implementation of business data is in the work of the Cincinnati Zoo. Now famous for shooting a Gorilla that has become the face of a movement, the Cincinnati Zoo was once famous for successfully applying a program of clever data aggregation and management about it’s customers and using it to design programs, work practices, opening hours and specific membership packages to boost it’s business.

The Zoo’s new ability to make better decisions about how to optimize operations has led to dramatic improvements in sales. Comparing the six-month period immediately follow- ing the deployment of the IBM solution with the same period of the previous year, the Zoo achieved a 30.7 percent increase in food sales, and a 5.9 percent increase in retail sales. (Lauren 2013)

Here we see an exceptional use of smart data analysis to identify the trends of customers. By monitoring the behaviour of their customers they could predict actions that they would take and alter their behaviour to fit these customers. What more businesses seek however is to change completely the shopping habits of their customers. One such company is Target.

This tale of the shocking accuracy of Target’s business intelligence unit is chronicled in Charle’s Duhigg’s New York Times article “How Companies Learn Your Secrets”. The Target Corporation is the second-largest discount retailer in the United States, behind Walmart, and is a component of the S&P 500 Index. It generated $73.78 billion in revenue last year alone. But while Target sells nearly every type of consumer product imaginable they found that for many female customers they were just a place to come and buy cleaning products. Target wanted to break this association with shoppers and so hired analytics whizkid Andrew Pole.

Shopping is a habit. There are times when it’s a treat to get a new pair of whit sneakers or a fun new video game but for the most part it is  a regimented habit carried out for survival. That’s how the brain views it and that’s why we become ingrained in the process of buying the same goods in the same place. This habit can be hard to break once formed. One time however when we are vulnerable to such a shift is when we have a baby.

At this point in life people are so flustered and vulnerable that they are open to suggestion and manipulation in where they shop by simple convenience. If you can get new parents into Target to buy a stroller, then they might as well pick ip the diapers there and the baby formula and the beer etc.

With this goal in mind Pole developed a formula using customer purchasing habits measured against mothers who registered with their maternity department to analyse the shopping habits of expectant mothers. Their products of choice? Cotton balls, unscented soap and multi vitamins.

From examine these purchasing habits Cole could predict which customers were entering their second trimester and send them out a discount booklet with baby products mixed in with regular products. Subtle enough  not to arouse suspicion but forward enough to entice the shopper.

The plan however back fired when a target branch got a call from an irate father demanding to know why his teenage daughter had received vouchers for a baby carrier, stroller and formula. “Were they encouraging her to get pregnant?” The manager made a follow up call several days later to apologise yet again only to be met with an apology and something along the lines on “I need to learn more about what’s going on in my own house”

Business intelligence can create smart business opportunities but can also make you look pretty dumb.

Appendices:

Duhigg (2012) http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?_r=0 Accessed 12/09/2016

The Beauty of Master Data Management

In a world with ever more data being generated and ever more functionality to be attend from that data, the importance of maintaining that data has never been so crucial.Maintaining uniformity across all machines in different countries, timezones and departments can be a nightmare for big businesses. In order to gain the maximum benefit from this data and to be able to use it in the most efficient way a business needs to implement a Master Data Management Program.

Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. 

Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.(Gartner, 2013)

Knowing that we need to keep track of our data is a no brainer. But how do we define this data?

There are essentially five types of data in corporations:

Unstructured — This is data found in e-mail, white papers like this, magazine articles, corporate intranet portals, product specifications, marketing collateral, and PDF files.

Transactional — This is data related to sales, deliveries, invoices, trouble tickets, claims, and other monetary and non-monetary interactions.

Metadata — This is data about other data and may reside in a formal repository or in various other forms such as XML documents, report definitions, column descriptions in a database, log files, connections, and configuration files.

Hierarchical — Hierarchical data stores the relationships between other data. It may be stored as part of an accounting system or separately as descriptions of real-world relationships, such as company organizational structures or product lines. Hierarchical data is sometimes considered a super MDM domain, because it is critical to understanding and sometimes discovering the relationships between master data.

Master — Master data are the critical nouns of a business and fall generally into four groupings: people, things, places, and concepts. Further categorizations within those groupings are called subject areas, domain areas, or entity types. For example, within people, there are customer, employee, and salesperson. Within things, there are product, part, store, and asset. Within concepts, there are things like contract, warrantee, and licenses. Finally, within places, there are office locations and geographic divisions. Some of these domain areas may be further divided. Customer may be further segmented, based on incentives and history. A company may have normal customers, as well as premiere and executive customers. Product may be further segmented by sector and industry. The requirements, life cycle, and CRUD cycle for a product in the Consumer Packaged Goods (CPG) sector is likely very different from those of the clothing industry. The granularity of domains is essentially determined by the magnitude of differences between the attributes of the entities within them.(Walter and Haselden, 2006)

This Master data is some of the most important data the a company can gather. But as more and more data is generated in the everyday business processes of a company, we need to have systems in place to process and store this data in an effective way the allows those in the company who can benefit from it to have access and to protect the often sensitive information that is collected in the day to interactions between companies and consumers.

In order to enable MDM we need to follow process of

ETL: Extract, Transform, Load. A process in database usage and especially in data warehousing that performs: Data extraction – extracts data from homogeneous or heterogeneous data sources. (Wikepedia 2016)

EAI: Enterprise application integration: the use of software and computer systems’ architectural principles to integrate a set of enterprise computer applications. (Wikipedia 2016)

EII: Enterprise information integration, is the ability to support a unified view of data and information for an entire organisation.(Wikipedia 2016)

For a layman breakdown of these terms refer to this handy guide:
http://www.b-eye-network.com/blogs/linstedt/archives/2005/09/eii_eai_and_etl.php

All of these processes come together to store information in a uniform manner that accounts for repetition and keeps data uniform, separate and accurate when applied in tandem with a strong data governance program.

Apendices
Source: Gartner (2013) http://www.gartner.com/it-glossary/master-data-management-mdm Accessed 26/05/2014.

Walter and Haselden (2006) https://msdn.microsoft.com/en-us/library/bb190163.aspx Accessed 12/09/16

Wikipedia (2016) https://en.wikipedia.org/wiki/Extract,_transform,_load accessed 12/09/16

Wikipedia (2016) https://en.wikipedia.org/wiki/Enterprise_application_integration accessed
12/09/16

Wikipedia (2016) https://en.wikipedia.org/wiki/Enterprise_information_integration accessed 12/09/16