Customer 360 in the Cognitive World 


There isn’t a company out there that doesn’t want to see a full view of customers across all channels . I have done large and small implementations of those in a variety of technologies . Several ISVs – Hadoop companies , NoSQL companies and traditional data warehousing companies – have all built big chunks of their businesses on the idea of Customer 360 . 

For structured data and with sources that don’t change a lot , doing a 360 view in a traditional data warehouse is adequate and in fact has significant performance and maintenance advantages . As data becomes more and more multi-structured and prone to frequent change , a NoSQL database starts to become a great choice . When large datasets come into play in these situations – especially on the multi structured side , Hadoop has advantages . 

There are trade offs too – everything that works with standard tooling (BI, administration , ETL) etc don’t work in NoSQL and Hadoop the same way as it does in relational systems . The licensing models have a big impact on decisions too . And while interoperability is much better today – it is not yet at a level that relational, NoSQL and Hadoop all work together happily . I am sure it will get there in some time – but not today or tomorrow .

That is just the back end story – the data management side – where decisions are made on the 3V model . The 3V model is splendid , but it is not sufficient once we look beyond the data management side towards the insights side of the equation . There I think of two other Vs – Veracity and Value of the data . 

The universal truth about data – especially big data – is that it is usually not clean . For insights to come out of that data – we need more tools to cleanse and govern . And then for getting value we need to rely on BI tools . If you think the job of a data scientist is sexy – you haven’t been in their shoes . Vast majority of real data scientists spend most of their time cleansing and rearranging data before they can model scenarios effectively . It’s grunt work and mostly not fun . There are great tools which can do cleansing and help with some governance – but it’s an uphill battle 

On the value side – there are many different types of BI tools depending on what you want to do with the data . For the most part , these tools need a human user to define what needs to be reported on . Various things like massively parallel processing across multiple cores , cheaper memory , more push down of functionality to databases all have helped tools become more efficient . A whole category of visualization vendors have made BI pretty exciting too . 

So between all the data management and BI options – we can already have a pretty good view of customer 360 today . So what is next ? 

Enter Cognitive computing !

  

Some of the existing visualization products support the idea of exploring elementary correlations of a given data set , and that is great. The idea is to have a human user then look at them , try different combinations and see what makes sense . Cognitive goes a few steps further and helps formulate questions that the average users hasn’t yet thought of asking . It can find and surface a lot more hidden relationships across data from all channels . For example – by analyzing news and social media , a customer can be flagged as a credit risk even though inhouse CRM and ERP data show them as having good credit . 

What about making sense of voice and pictures and video to enable better customer service ? What if a robot can converse with you and give you all the answers you need based on its cognitive abilities ? I am not kidding – take a look at this video of a cognitive robot dancing when it is asked to. 

Even if we discount robotics (which we really shouldn’t discount )  , Figuring out a customer’s intent via voice and facial recognition and responding based on data analysis takes customer care to a whole different level . Imagine calling your favorite cell provider with a question “when is my contract ending” and software figuring out from your tone and historical data that you have abysmal coverage in your office and hence you are a customer who will potentially churn quickly. It can also figure out that in your area there are several other customers who have the same issue . The system can then alert the field services team to fix coverage issues , as well as offer you an incentive to stay loyal to your provider . Or the system can decide that you need to talk to a human agent and transfer your call with all this intelligence to the best service rep to take care of you . That is the power of cognitive customer care. 
Responding by voice and touch is great , but it is becoming a given in modern UX. What Cognitive adds to make the UX even better is asking questions about the rationale behind the system’s decisions . You can also ask the system what confidence it has on its decision and explain to you other possible solutions . Imagine engaging in a customer chat about your phone heating up. A cognitive system can help you trouble shoot by yourself . The same system can help a service engineer trouble shoot the network and offer different solutions and the odds for each . The picture below is such a cognitive trouble shooting  and debugging app !

  
Another interesting aspect is helping trade off decisions for a customer when there are several options to choose from – like choosing a phone , the right rate plan and maintenance options .

The one last aspect to consider is about reuse of existing customer information systems . There is good news there too – Watson can query those at run time to make decisions . And if there is an API to commit a transaction in a system , Watson can trigger it too (like say update your billing plan) . Not all data needs to be physically persisted in Watson for it to work .

It’s an exciting new world of possibilities that cognitive opened for us . Plenty of customer projects are already in progress too. Let’s just say I am in geek heaven 😉

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Speed of Transformation


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“How fast should you transform your business?” . This is my favorite question to ask business leaders these days . I am yet to run into even one leader who told me that they have time on their side – every one wants to transformright now, which is absolutely great to hear. This is also totally in line with what my analyst friends have typically opined too over the past couple of years. And yet – there are very few “all in” transformation initiatives in the world of large enterprises. Many leaders choose incremental transformation even though theyreally want to take a more aggressive approach.

It’s catch 22 on many dimensions for leaders of large companies to jump in with both feet.

Planning can be scary: Change is hard even on good days. It is much harder if you do it in an unplanned way. Waiting for every i to be dotted and every t to be crossed via detailed planning is not helpful either – you will risk missing the market opportunity.

Rules for startups don’t apply to you :  You want to be the next Uber . But your investors have a different yardstick to measure you, even if they too want you to be Uber. A large public company taking risks get punished in the market by institutional investors for putting profits at risk . The same institutions will yell at startup CEOs for worrying about profits instead of growth. Larger companies with diversified portfolios can take on a lot of risk if markets favored the approach -but that is not how it plays out for the most part. So leaders shift to the safe approach – contain costs as way to manage profit, as oppose to invest for future growth. There are notable exceptions like Amazon etc – but they are exceptions.

Ring fencing only goes so far :  A popular large enterprise strategy is to ring fence an innovation team to help them incubate and grow a new business without the organizational antibodies killing it in the bud. The typical result is that a bunch of amazing “proof of concept” projects will come out in fairly short time and show tremendous promise in pilot implementations. Yet – most of them don’t scale sufficiently into a big business. This happens for many reasons. For example – scaling needs a different mindset from “proof of concept”. It is a lot of boring grunt work that the “innovation gang” feels is beneath them. They would rather do the next cool proof of concept. The larger organization feels no ownership of the new idea that got thrown over the wall to them, and finds faults with it from the get go and kills it at the first opportunity. Again , there are a few exceptions – like say IBM Watson, but again – they are exceptions.

So how fast can you transform your business ?

Hard to answer, but I think empirically we can say that it is a function of how fast you can transform yourself as a leader.

Grey is the new black : Perhaps Grey was always the black, just that we didn’t give it the due credit 🙂 . You need an extreme ability to let go of the world of black and white and work in varying shades of grey. It is not easy and does not come naturally to many of us . Early leadership roles in most companies are all about tweaking prescriptive policies already in place, with very little encouragement for radical change.

Communication is the biggest weapon  : Transformation typically fails by default – you need to nurture it on a full time basis to let it thrive. The main reason it fails is because you don’t talk about it enough. Although they are exceptions, successful companies who could convince investors to go along have all had leaders who are excellent communicators. At least in theory , Investors like predictability – if you feed more information to them to make their models work, they tend to like you. External communication is not really the big deal in my opinion – many transformation initiatives fail mostly because of an abject failure in keeping the larger organization informed. I know IT organizations who thought they have successfully shifted to agile getting upset when business does not give them due credit for their transformation. In almost every case – it was a failure to keep the business informed along the way on what is changing and what is the big deal. Hanging banners with clever slogans in office does not substitute honest person to person communication

Best persons for the job : Nothing shows the level of commitment to transformation like the people you put on the initiative. This is also where most businesses fail. If your most proven folks get to run established businesses instead of your transformation initiative, then the message you are sending the larger organization is that 1: you are not all in, and there is no need for them to be all in either and 2: you don’t have confidence in your leadership bench to backfill the current top leaders. This particular aspect is close to home for me – a big reason for my coming back to IBM after a gap of few years was the type of people investments that IBM CEO was making on Watson, Analytics and other new business units. The leaders – from General Managers, to their staff, and to people working for them – were all accomplished folks who had successfully run huge businesses before. That gave me tremendous confidence that IBM is serious about its transformation and that it is the right place for me.