Context Integration, the Future of System to System Interactions

Context integration is the future of system to system interaction. By prioritizing relevance, customer needs and jobs-to-be-done, context is the reason to operationalize big data.

Definition: Context integration is the instantaneous combination of information and process integrated at a point in time, location, to the right person, on the right channel and on the right device.

During the past 20+ years, the way in which system and/or application integrations have been conceptualized has not really changed all that much. Sure, it is possible that I am being overly simplistic, but up until now, there have been only two types of integrations process and data. Yes, the protocols have changed, APIs, REST, SOAP – pick the acronym, and designs changes (spoke and hub, point-to-point, bus,etc.,..). However, what is often assumed is that a person will make the determination as to why a particular data element is on a screen, or not. Now, there is too much data, too much information it is time to refine the process.

Data Integration

Data movement in one direction is the easiest (not always easy, mind you) type of integration. Yes, there are nuances, but overlooking these nuances puts the complexity on the low end of the spectrum. One directional data integrations are typically read-only (or a copy). For example, taking data out of an operational system and putting it into a reporting system (I am not talking about transforms just yet). If you desire the data more quickly, say real-time, slide the complexity to the right a bit. Want to be able to write/update and have this reflected in the source system; bidirectional, slide the scale bunch more to the right.

Action item: we need to progress from data integration to information integration, there is too much data, people need information.

Process Integration

Process integration often require detailed use cases, user scenarios and can often be quite complicated. Process integration is best described by old school triggers. Something happens in system A, but the users on System B need to be both alerted, they need to do something and hey need to know what to do. Too often, this type of integration ‘channel jumps’ and the recipient receives an email, text or page in order to go take some action, in some other core system. These types of integrations take place in everything from sales, to support, operations and marketing, as well as everything in between.

Similar to the data integration conversation, when it is one direction and the originating system does not need to be notified upon completion, complexity is reduced. Now, if there are multiple process flows in the secondary system, and each is complex and the originating system needs to be aware at each stage (think credit check, for example), slide the complexity scale a bit to the right some more.

Action item: We need to move beyond the task list of things to do, to being told what to do, how to do it and when to do – why? only if asked.

What does Context Integration Look Like?

As stated above, context integration is information plus process, it is real-time, but may or may not be bidirectional. What I mean is that communication is bidirectional, but it might not be operating on the same data. Delivering the right information to the right person at the right time is hard, just start by sliding the scale way way to the right. For starters, there is now a third system involved within each integration scenario, the analytics engine. Breaking it down further:

• Information equates to ‘what’,
• Process equates to ‘where’ and ‘how’;
• Context equates to ‘why’, as-in ‘why is this important to me, now’?

In order to accomplish this feat, we need more insight. We need to spend a bit of time translating data into information, processes into specific tasks and actions and help the user to understand why something is displayed or being done. In a very real way, right time information may also be considered to be proactive, as expectations are low in this area, but changing rapidly.

The two primary systems and their users need intelligence, something that has been done by humans, until now. The possibilities are awesome, the complexity enormous, the risks, very real. The intelligence comes from the aggregation of social data, combined with filtering, analysis and direct (ie predictive) insights. The salesperson wants more than just new information, he/she wants the question they forgot to ask – don’t only tell me something new, suggest what I should do.

The following are just some quick ideas, there are so many more and if you would be willing to add your own, I would appreciate it!

Example – Sales

• Data – The CRM (SFA) application has a copy of purchase and/or case history, maybe event data, purchase history and company financial information

• Process – The Marketing Automation System responds to a visit by a lead to landing page a task is created to make a call or send an email

• Context – The intelligence platform creates a set of tasks, based up information from Linkedin (say through InsideView integration) that certain people are active on Linkedin and have changed jobs, company purchase history and trends are used to suggest tone of message and 3 independent tasks are created. If the CRM system notes the user is accessing information on an iPhone, the tasks are delayed a few hours, as the emails and tasks are better done on a larger screen. Tasks and reminders are created and scheduled.

Example – Service (Customer Support)

• Data – The Contact Center has account service history, household purchase history, number of claims displayed on the screen (or a couple clicks away).

• Process – Add to the above, notifications of device recalls, health alerts, community posts, credit checks, invoice verification, payment verification, (think billing and finance).

• Context – In financial services, think fraud alert. For example a user social check-in in New York and credit card use in Paris. In travel, make agents aware of weather or flight delays, tell client new flights are booked. Help systems should be product and location aware as well as being proactive.

Example – Customer (Me)

(There are too many customer examples to count, feel free to add your own)

• Data – Give me access to my account information through a portal or smart device

• Process – Notify me of potential fraud, account balance issues, credit issues, ask and wait for response. If an application is incomplete, point me to the place to complete it. If a doctor or hospital is too busy ask me if I want to reschedule.

• Context – Notify me of weather on my travel route, give me options: car; a new route, plane; a special number or email address, finance; tell me my bank account is low before the rent check is due. Tell me to watch out for an issue, before I have it – the customer side of proactive support.

Context, the Difference between Information and Knowledge

In my weekly routine, I try to strike a balance between academic thinking, practical thinking and the balance between the two.

Living in northern Vermont gives me the opportunity to create fun metaphors to think through complex topics, allowing me to add a bit of local color. December is typically a cold, dark and ‘stay inside’ kind of month around here. Yes, there is a little bit of last minute shopping to be done, but often the keyboard and Amazon suffice. However, there is typically little snow in December, thus no real good reason to go outside. As luck would have it, this year has been a little different, with 30 inches (75cm) of snow directly before New Years, kids sledding on the hill, me able to hit the slopes with my boys. This December was indeed, different.

What is the Right Amount of Information?

I am driving my daughter to gymnastics and present to you the following: it is 25 degrees Fahrenheit, snowing and there is 3 inches (~7.5 cm) of snow covering the road. I am traveling a meager 20 miles per hour. I ask, via twitter of course, if my foot should be on the accelerator or the brake, how would you answer me? Skipping the obvious, a stop sign or a car stopped ahead (It was a voice activated Tweet). Is the simple Tweet enough for you to answer my question?

What I am getting at here is that their are a few parts, first we have the data (temperature for example). Information then comes from assembling and analyzing the data. In this case, we have temperature, precipitation and road conditions. Knowledge comes first from putting the information together and adding context. It is snowing, the roads are covered and the temperature is not going to melt the snow. There is probably hard pack snow, on the roadway, underneath the freshly fallen snow. Wisdom is then applying experience and acting accordingly. I will try hard not to drive off the road, remembering that four-wheel drive is great for going, it does nothing for stopping.

In this situation, I am actually traveling up a hill, one way, (and down a hill on the return). This is an important piece of information, without it, an answer should not be given. So, it does depends which direction I am driving. If I am trying to make it up the hill, I need a little more speed. If I am going down the hill, I am hoping that the breaks do their job.

Translation to the Digital World

In the digital age, the difference between information and knowledge is important and it is going to become even more important. This is in no way an academic debate that I am trying to jump into, 20 years late. Many people, smarter than me, have given this discussion much more thought. What I am trying to suggest is that context is a critical piece of information, and without context all you are giving back is data, information at best. In order to present knowledge, information, data, insights and experience need to be in a continuous loop. This is especially true in the digital age of rapid communications. Teams need to think through as many scenarios as possible and make sure the context is carefully considered.

Looking at a Tweet, a Post, a Blog, a Picture or a Status is only one bit of information, usually in isolation and not enough. Some would say it is only one bit of data not even information. The capability to respond, engage or communicate on social channels requires access to information (what is the right answer), but beyond that is ‘How’. It requires experience, insights and, yes, context. What has not changed is that answers, right or wrong, travel far and wide. Context is the idea that the information shared is relevant, in both time and situation to meet the needs of the person asking. In the scenario above, telling me that the car is certainly capable of 140 miles per hour is not an incorrect statement, but it does lack relevance.

The call to action is to make sure that your people, processes and technology are up to the task.

Trusting Data Versus Trusting Your Gut

As if the CIO did not have enough to worry about; Cloud, Social, Mobile, along comes Data  (BigData to be buzzword compliant). OK, I might have it a little backwards, Data has been a concern for a long time, but now, because of Cloud, Mobile and Social, Data is an even bigger challenge. The list of issues surrounding data is a long one; growth of, quality of, management of, storage of, interpretation of, access to and last but not least, analysis. Many of these are technological, but the real issue is when data crashes into a human…

Do You Trust Data or Do You Trust Your Gut?

The stakes are real – the future of your business. Leveraged well, data will provide an edge, properly used it is a difference maker.  Do phrases like: ‘My instincts got us here, and we are doing just fine’ or ‘it feels right’ fly around your office? Hyperbole, maybe, but most of us know the type and have experienced at least a bit of it. There is an argument that suggests that some people actually do know what the data says, and their ‘gut’ is right. As for the rest of us, I am not so sure, the answer is that balance is needed. According to HBR (Full source below), that balancing has a name – an informed skeptic:

At one end of the spectrum are the pure ‘trust your gut’ types on the other, the purists (“In god we trust, everyone else bring data”) types. The basis of the HBR article is: even if the data is good, decisions based on that data should be questioned – ie, be a bit of a skeptic. This is interesting and important.

“The ability to gather, store, access, and analyze data has grown exponentially over the past decade, and companies now spend tens of millions of dollars to manage the information streaming in from suppliers and customers.”

From my perspective, it is all about intelligence; using data, properly, to provide you and your business insights to make decisions. That is what you do, right; the data is there, everyone who needs it has access and the entire organization is leveraging it to its full potential? As the article also suggests, IT should spend more time on I, less on T – while it sounds fun, there is a small point there, not as big as the author makes it seem. To question data, to invite skeptics, everyone needs access

Do People Really Know What to Do with Data?

What are the reasons that data seems to scare people. Few will admit to being scared by data, but very few have the real background to argue on empirical terms when charts and graphs and conclusions are put in front of them. An IBM/MIT study (Source 3)  identified three levels of analytical sophistication: Aspirational, Experienced and Transformed, in a Year-to-year comparisons of these groups (which can be seen in the source report) it shows that Experienced and Transformed organizations are increasing their analytical capabilities, significantly.

(note: The IBM/MIT report did not present the information in the format above, I used the article to create an image similar to the HBR article).

“The number of organizations using analytics to create a competitive advantage has surged 57 percent in just one year, to the point where nearly 6 out of 10 organizations are now differentiating themselves through analytics” IBM/MIT

What is unfortunate is that it sounds better than it really is. If you really start to dig deeper into the data (oh, the irony), the story is a bit more complex. While things are getting better, I am not sure I would characterize them as ‘good’. Out of curiosity, I wanted to look at a topic important to me, Customer Experience. Based on my interpretation of 3 sources of information, many know what to do, but are struggling to do it. By my read of the IBM/MIT report, only 1/2 ( 10%) of the organizations who ‘really get it’ (transformational) are using analytics to make decisions regarding customer experience.  Turning that around, 90% are not, scary, unfortunate, reality.

“Typically, an organization’s highest-spending customers are the ones who take advantage of every channel, whether it’s the web, a mobile device, or a kiosk on a showroom floor.8 Unfortunately, these customers are most at risk for experiencing a disconnect in navigating channels that are not yet integrated. A unified multi-channel “bricks and clicks” approach can allow customers to move between website, smart phone app, or an in-store service counter with a consistent quality of engagement.” (Source 1)

The only way to know and really understand something like this is to have the data to prove it! It is not rocket science, but it does take some work. What steps are you taking to share data, train people and leverage what you have right there in front of you?

Conclusion

• Something as valuable as Data is not a Problem, it is powerful and valuable Asset,
• Help people to understand data, encourage them to be an educated skeptics (yes, question that Infographic)
• Gut Instincts are not bad, just keep things in perspective, right place right time,
• And for goodness sake, start using Data to better understand your Customers!

There is so much more to this story. In writing this post, I have a whole new level of respect for this topic…I hope you do too.

1. Analytics in the Boardroom, IBM Institute for Business Value, Fred Balboni and Susan Cook
2. Good Data Won’t Guarantee Good Decisions, Harvard Business Review, April 2012, Shvetank Shah, Andrew Horne, and Jaime Capellá
3. Analytics: The Widening Divide How companies are achieving competitive advantage through analytics, MIT Sloan Management Review with IBM Institute for Business Value, David Kiron, Rebecca Shockley, Nina Kruschwitz, Glenn Finch and Dr. Michael Haydock

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.