Kemofarmacija is the leading wholesaler of healthcare products in Slovenia, offering customers more than 16,000 different products ranging from medicine and medical devices, to nutritional and cosmetic products. Though we have enjoyed the industry leadership position, we only recently began to fully exploit our online sales channel. It was an area that we knew required analysis and insight of our growing data volumes – our Big Data. 

While more and more of our customers were looking to make their purchases online, our competitors were over taking us with more aggressive web-driven sales models. We were not maximizing opportunities for cross-selling or more targeted online marketing and on top of it all, our website was simply not easy to navigate. Our challenge was as simple as it was profound: update our online operation to meet the needs of the evolving market.

With the IT and marketing divisions working side by side we envisioned a plan to use technology to drive more profitable sales – through tailored website advertisements and promotions, offering customers the products they wanted at the right time. But, for this plan to be successful, we first needed to gain greater insight into our online customer base. 

What we needed were insights to our customers’ preferences. We needed to mine, sift through and analyze our Big Data. Then, based on these insights, our marketing teams could promote specific products for strategic time periods and fine-tune the forecast for marketing campaigns.

Working closely with IBM and its partner MZR we designed a new IT infrastructure capable of giving us the advanced analytics and cloud capabilities we needed to transform our business. 

Now, utilizing an IBM Power System server running WebSphere and other key software tools, we are able to offer our customers a personalized online shopping experience, automatically promoting different ad spots to different customer segments. This capability has significantly increased the amount of sales orders placed through the company’s web portal, and the average customer-spend per visit. Measureable results include: 

• 30 percent increase in online sales order lines;  
• more than 90 percent of all sales order lines now processed through our web portal;
• improved cross-sales by presenting more targeted product recommendations, encouraging customers to buy more during every visit; and
• as a result of the increase in online sales orders, we have been enabled to transfer a number of staff from our call centers into higher-value areas, such as field sales and marketing. 

Added together, these results have translated into sustained market leadership, with an estimated 42 percent share of the market.

I believe the best medicine for companies right now is to think about the role that Big Data analytics can play in their business. If they fail to transform in this way, their competitors will figure it out, and by then it will be too late.

How to build a Career in Analytics – this is a question that I have been asked umpteen number of times by a lot of people! My answer to most of them has been that Analytics is all around you – you just need to seize the opportunity to apply Analytics in the world of business. Now, this may seem like a motherhood statement, made with the intention of not providing any concrete guidance on how it can be achieved. But, truth be told, the opportunity to make a switch to Analytics as a career, beckons now, more than ever.

Almost every major consulting and research firm on the planet have understood the far reaching implications of Analytics and have started creating teams to prepare themselves for the opening of corporate floodgates to embed Analytics in their day to day business decision making processes as well as to shape their strategic thinking. There is a huge shortage of people skilled in Analytics who can help corporate houses make the most of data that is being stored and generated at a frenetic pace.

Here are my 5 tips for entering an Analytics Career

1. Learn the tools of the trade- SAS, SPSS, R, and SQL. Start with any tool that you can get access to. Sometimes you will be surprised to find that a Tool that you thought did not exist in your organization actually does. In one of my previous jobs, when I was busy negotiating with SAS for licenses for my team, a colleague of mine, who was an Actuary told me that he had seen a SASsession in one his team member’s PC, sometime back. I followed up with that team member and we found that we had a SAS server already in place waiting to be used!

Learning is not about knowing everything, but learning substantial portions thoroughly and gaining sound knowledge about what you learn. I would much prefer a candidate who knows a lot about how to run a regression in SPSS, than a person who has half-baked knowledge (knows a little bit about CHAID, done a little bit of regression,knows a little bit of SAS and a little bit of SPSS) If you can muster one tool and a few modules/techniques of the tool, then you stand a better chance of getting a job and also of being able to get a job done.

Pick up a tool that is available easily to you and start learning it – SAS, SPSS, R (now available as open source).

I do not recommend using pirated software though they are now openly available in the market.

2. Learn the tricks – If you have learnt the tools, your job is only half done. You need to learn the tricks of the trade. Now there are two options before you- a) Learn from another experienced person/s who maybe there in your organization b) Learn from professional curriculums.

The self-help tutorials will not provide you the secret sauce of Analytics which is very essential for being able to deploy Analytics to solve real life problems. The outputs from running procs in SAS or models in SPSS throw up a large number of statistics. Knowing which statistic to look at and which ones to ignore is one of the most important secrets which only seasoned Analytics professionals will be able to share.

3. Look for an opportunity in your sphere of work to apply Analytics in your present organization. Quite often, people find it difficult to identify where to start. The simple rule of thumb here is to identify sources of data and see if data is being collected in some data repository. If data is being collected in a certain business process or function, the chances are that it is waiting to be used.

Always remember that it is helpful to start with the low hanging fruits. Do not try and build a predictive model at the first go. Your organization will not be ready for such a sudden large step change; more importantly, you will have to earn the trust of the organization before they begin to trust the predictive power of Analytics and of your ability to harness it.

Start by generating simple insights from the data which is not presently captured in the business reports. Create simple metrices which will add tremendous value to the businesses and will get the important people in your organization interested in what you are doing. I was once speaking to a client of mine(who were in direct selling) who had the best BI system in place, but they did not have well defined metrices which could help them leverage the BI system. They did not even know simple facts like:

• Which regions had the maximum demand for their products and needed the presence of a bigger sales force team?

• How are customers responding to promotions?

• Which types of promotions are more successful?

• How many customers and which customers had dual/triple ownership of their products?

• When was the last time that a customer had bought any of their products? ( one year back, 2 years back, 3 years back, 4 years back, 5 + years back)

• Customers typically upgraded their product after 3 years- which customers fell into that category?

My intention of highlighting the example above is to drive home the point that most organizations do not even do the most obvious things from a data analysis perspective.

The best way to start Analytics in your organization is to start by asking some simple and obvious questions, both from a shareholder/management point of view as well as from a customer point of view. Once, you have a list of questions and facts that you would like to see, start using the data and see if you can come up with those facts and/or answers to the questions that you have.

The next stage is to convert the facts into reports which can be generated for different time intervals and for different slices and dices of data. When you have done so, you have already started building a BI system in place. Once, you have a set of reports that show important and engaging facts about the business and have insights and answers to questions that any manager would love to know, you have already built a case for yourself to start using Analytics in your work/organization.

4. Make a case study of your work and show case to the top management. Else, add it to your CV. If your organization is not supportive of your Analytics initiative, look outside in the relevant domain. There would be plenty of opportunities outside for a person with your new found skills!

5. Read plenty on Analytics – Join blogs on Analytics, Analytics threads, follow Analytics companies and keep abreast of the latest happenings in Analytics. This will keep you well positioned for keeping a track on how Analytics is being applied in different business domains and functions and increase your knowledge in the field.

Being true to the magic number 5, here are 5 possible career paths that you can chose in Analytics.

1. Tools expert/expert programmer: Experts in programming and nitty-gritty’s of the software. You can become the go to person for any programming related queries and software troubleshooting.

2. Expert Modeller – more often than on, the best programmers are not the best modellers and vice versa. That is perhaps, because you need different kinds of temperaments for these two different skill sets.

3. Solutions Expert – conceptualize and create Analytics solutions to help solve business problems. A Solutions expert understands the problem to be solved and has the expertise in creating the most appropriate Analytical framework to solve the problem. They also recommend the best method/sets of methodologies to be used to solve the problems. They are the “Analytics Architects” if you can call them so.

4. Story Teller – you are able to create the most practical, impactful story that helps clients change their businesses. You have the ability to understand the business of the client, their pain points and pull together insights from the Analysis to weave together powerful strategies for the client.

5. Analytics Salesperson – your job is to convince prospective clients to use analytics in their business and show them how they can benefit.

Let me add a 6th one. Give back to the Analytics community- when you realize that you have learned enough, start dissipating that knowledge to the larger community. The more people become aware of the power of Analytics in career, the more they will adopt it and start using it.

Our world has never been more interconnected and instrumented. Streams of data are constantly being collected from sensors that monitor everything from the environment, vehicles, buildings and bridges, mobile devices and home appliances. And because of these constant streams of Big Data, opportunities exist to effectively predict when equipment will fail, a storm will hit, traffic will increase or milk will spoil.

Human beings are still the most important and sophisticated data generators and sensors; however, our interpretation of signals is still highly unreliable.

That is why companies across every industry are using predictive analytics to collect, assimilate, and analyze the Big Data around behaviors that we humans reveal on a daily basis. By understanding how people act in different environments (offline vs. online) with what they purchase, along with what they say, how they say it, and when they say it, determines what their next action might be. More specifically, it can determine how these actions are directly affecting everything from the manufacture of equipment to inventory levels; and from fraudulent activity to the spread of infectious diseases.

For example, cars are equipped with sensors to monitor the engine, brakes and safety devices, among others, but more and more car manufacturers are incorporating “people data” into how cars are designed and manufactured.

Driving styles and driving environments vary. There is quite a difference between Chicago and Paris. With usage monitoring systems, car companies can better understand how the stress on a set of brakes can impact longevity as well as supply in the auto part aftermarket, and when maintenance, repair and overhaul might be required.

Social media is also an important element in this process. Think back a few years to when a large automotive manufacturer recalled millions of vehicles to fix a host of issues. By listening to the conversations of its customers in the social sphere, it might have been able to catch these issues early, alter the manufacturing process to fix quality issues, be more proactive in responding to customer complaints, and avoid a major crisis communications issue.

The effects of monitoring social media are also extending further into healthcare and having a direct impact on when and how fast diseases spread. Based on people’s tweets last summer, Twitter accurately predicted – eight days in advance and with 90 percent accuracy – when and where people would get sick from the flu.

Human sensors are also impacting the sports world when it comes to staying healthy.

The Leicester Tigers, nine-time champion of the English rugby union’s Premiership, aremonitoring all facets of a player’s activity – on and off the field – in order to reduce players’ injury rates. It’s a human form of predictive maintenance.

After all, losing a key player for an extended period of time not only hurts the team on the field, it can result in reduced ticket sales and spectator attendance if the team does not perform up to expectations.

With monitors built into their uniforms, Leicester collects data on fatigue and game intensity levels that allow coaches and trainers to design a personalized training regime for each player tailored to his physical and psychological states, reducing the risk of injury.

Even though humans can often be impractical, impulsive and evasive, their actions speak volumes.

The next time you visit the doctor, climb into your car or tweet an opinion about your favorite brand, consider all of the data you are generating and how that data is being fine-tuned to improve your life – and the lives of those around you.

Please join me on People for a Smarter Planet Facebook page on Wednesday, Jan. 23 for further discussion on this topic: https://www.facebook.com/events/553247638020232/

Cisco is looking to add more intelligence to WiFi networks to give businesses and service providers more insight into customer behaviors.

Cisco Systems is buying startup ThinkSmart Technologies, whose location analytics technology will enable enterprises and service providers to better understand what is going on inside their retail stores and to more easily give their end users what they want, according to company officials.

Cisco executives see ThinkSmart's offerings as a natural extension of their strategy of adding greater intelligence to the network--in this case, WiFi networks--through such capabilities as analytics. The networking giant announced the acquisition Sept. 26; no financial details were disclosed.

ThinkSmart, located in Cork, Ireland, will become a part of Cisco's Wireless Networking Group, and will be integrated into the company's Mobility Services Engine.

"Together, Cisco and ThinkSmart will enhance the wireless network by providing location intelligence and analytics to service provider and enterprise customers to know what is happening in their environments and to better engage end users," Hilton Romanski, vice president and head of corporate business development at Cisco, said in a Sept. 26 post on the Cisco blog. "Cisco's vision for mobility solutions will be accelerated by the acquisition of ThinkSmart, enabling customers to analyze location data from wireless networks and provide insight that can be used to drive new commercial opportunities and enhance end-user experiences."

The rapid growth in the use of mobile devices, including smartphones and tablets, gives businesses and service providers an opportunity to get a greater understanding of what their customers are interested in and how they can best engage those end users, Romanski said. Location analytics capabilities are a key to gaining that insight, particularly in such public places as retail locations, hotels and airports, he said.

Using ThinkSmart's technology will enable businesses to collect a variety of information from their venues, from the time of day to traffic patterns to how long end users stay in the stores. With that information, businesses can then target the end-user experience by ensuring appropriate staffing levels, cutting down on the amount of time customers spend waiting, improving the flow of customers and optimizing business processes.

Combining all that with Cisco's networking capabilities will only enhance what enterprises and service providers can do, Romanski said.

"The acquisition of ThinkSmart reinforces Cisco's commitment to deliver an intelligent network by providing customers with enhanced tools, such as location analytics, that increase the value of the network," he wrote. "This aligns with the core, one of Cisco's five foundational priorities, by providing differentiated solutions within the infrastructure of the network."

Cisco executives for the past few years have been talking about the rapidly growing numbers of Internet users and mobile devices that are on the horizon, and how that growth is driving the increasingly huge amount of Internet traffic. According to a Cisco survey released in May, there will be 3.4 billion Internet users—or about 45 percent of the world's population—by 2016. Also by that year, the percentage of Internet traffic generated from PCs will drop from 94 percent in 2011 to 81 percent, with other devices like smartphones and tablets increasingly generating more.

WiFi networks also are growing in importance, with more than half the world's Internet traffic coming from WiFi connections, Cisco found. In addition, wireless carriers concerned about a looming bandwidth crunch on their broadband networks are looking to WiFi networks to ease that burden by offloading some of that traffic.