1 sas.com Page 1 Copyr i ght SAS I nsti tute I nc. Al l r i ghts reser ved.

Transcription

1 1 Page 1

2 Why invest 30 minutes of your time here? A good analytics capability is hugely valuable and becoming more of a differentiating mechanism in organsiations However the ability to differentiate and to achieve value with analytics is not guaranteed There is something emerging as an approach to dealing with many of the challenges in delivering analytics An example of some work we are doing with a customer to find another way Page 2

3 About sensing and about speed/agility Page 3

4 [2] The topic is Analytics for everyone, keeping an open mind so what exactly is analytics? This is a recent definition of analytics from Gartner. I think you can appreciate that it is trying to cover a lot of ground. I really like the part about BI vendors using analytics to differentiate their offerings I only have 27.5 minutes so I am not going to subject you to my reading of a long definition. I have a shorter version I like as it talks to the outcome rather than the inputs This may mean different things to different stakeholders Finance may see it as accurate cash flow projections into the future based on complex business machinery Manufacturing may see this as autonomous control of robotic assembly Retail may see this as customer recognition using CCTV footage Government may see this as identification of at risk individuals before they become risky and therefore more effective to address Page 4

5 [4] Analytics is everywhere. At the heart of analytics are these 3 things, data, discovery and deployment. This aligns with the definition is showed earlier Data the fuel and lifeblood of analytics and also the largest cost in many instances Discovery the process of finding interesting things in the data Deployment the key to achieving the value in the previous 2 by getting it used Analytics is not unique to an industry or an organisation function. Analytics is found in all industries financial services, retail, logistics, insurance, manufacturing, online, government at all stages of the value chain from resource extraction through manufacturing to distribution and retail To all departments and functions in the organisation finance, marketing, sales, distribution, inventory, operations There are some key drivers for analytics in organisations today at a high level Regulatory and risk many industries now need what would have been traditionally called advanced analytics as a starting point. Risk management in banking has gotten to the point where some of the most advanced uses of statistical modelling occur in that industry in that function Innovation %, %, % -> expect greater than 70% by > staying ahead Customer experience - Mid 90 s CRM and managing customer relationships - Early 2000 NPS and the measurement of customer satisfaction - To understanding of customer experience in their shoes Hypotheses - Examples at certain life stage have certain needs customers have a lifetime value don t over or under invest in customers, know what they can be worth Into market - Our hypothesis about needs at life stages test them out via Digital transformation - Or rather digital execution That create feedback information That shape the hypotheses of customers Point of analytics is this loop, the lifecycle, the insights loop Market sensing and response system kind of like a central nervous system for companies Page 5

6 [7] MGI study 2011 value realisable from analytics 2016 value achieved is only part way there (certainly less than 50%) despite massive investments in getting there HBR Dec 2016 referring to the 2016 MGI study Gap launch and embed this is the cycle from ask to implement Because analytics crosses organisational silos the ability to manage a process across this silos is severely limited Generation and collection of data this is the heart of what I referred to as the insights loop Differentiation those who can do it better will get the results faster -> if it were easy it would have no power to differentiate in a competitive market Page 6

7 [9] Analytics is hard Again reinforcing the lifecycle most value will be achieved when this can be created Mandatory vs discretionary different difficulties So where did it become hard? Technology landscape This is my own personal experience of doing this Implications Building analytics vs doing analytics Page 7

8 [11] So how do you avoid these problems Eliminate friction in the process Which helps support a cohesive well governed and Integrated system for generating insight and harvesting the signals that come back to you Page 8

9 [13] The way to do this is through an insights platform Emerging term Data management collect, aggregate, analyse, prepare deploy in the same environment Analytics perform all analytic types on the same dataset without moving it around Take the discoveries from the analytics process and get them to a point of use quickly, efficiently and well managed Harvest the signals coming from the feedback loop to enable a test and learn, or better a sensing system We have seen this provide Scalability ability to rapidly respond to changing needs Agility take the learnings and apply them to future actions Speed business ability to get results, not speed of execution althought that is also part of it Resiliency Cohesion key to getting value is across the business which also leads to Governance Accuracy is a given! Page 9

10 Page 10

11 [19] this is an example of a technology designed to support an insights platform Source-based data engines leverage the embedded process technology, compute capacity at the source. This also allows us to do model retraining key to machine learning at the point of generation.. MPP approach that leverages memory where possible and intelligent spillover where not Cloud based so it is flexible and easily scalable Our thought is that you shouldn t have to place a technology bet for where your analytics have to run you should be able to choose and change as needed. SAS existing solutions and net new ones are being built on SAS Viya. In addition, we have REST APIs to include SAS Viya actions into your existing applications. And while you can still come through the SAS door to access SAS Viya, you also now have direct access from Python, Lua and Java as well. Our goal is to provide an architecture that s understandable to the Python programmer just as easily as to the SAS programmer and within one, governed environment. 11 Page 11

12 So we can now see the importance of being OPEN, let s talk about the strategy. We often talk about our personas that we are targeting with our messaging one of the the primary pieces we will be targeting are the data scientist and software developers. We can see that the data scientist is aligned with programming, visual applications, and solutions. And the software developer will be primarily interested in APIs, developer edition, and applications. We will touch on these things throughout our presentation. To kick us off, a HUGE key to the openness of Viya is our APIs. Let s look into that. Page 12

13 [23] So how does this help? Open to all means easier to integrate, easier to leverage Use all skills means everyone contributes Page 13

14 [25] Page 14

15 I am pretty sure most of you were barracking for the Iguana Messages from the video many competitors chasing the same customer, notice the customer got away based on competition Is it the snakes are too slow and senses are insufficient Is it the speed of the Iguana Is it the competition amongst participants that Fortune 1000 comment earlier rise and fall of significant companies is accelerating, how do you stay relevent Ahh forget the hidden meanings, it was simply an incredible piece of video Page 15

16 Leave behind messages Analytics is hard but have massive potential but has to be leveraged uniquely by each organisation To achieve value focus on the insights loop integration and cohesion Approach this with a platform mindset but also remember You don t want to build analytics, that is for software companies, you want to do analytics otherwise you are not focussing on your core strenghts Page 16

17 Page 17

18 Page 18

19 Page 19