0:04 good morning everybody and welcome to
0:06 the MSS business transformation
0:09 Institute's webinar entitled becoming an
0:11 intelligent organization is easier than
0:14 you think I'm Laurie Elliman and today
0:16 I'm here with our presenter MSS data
0:18 scientist dr. Luo hao
0:21 dr. how is a data scientist with
0:23 comprehensive experience in data
0:25 analytics and complex data visualization
0:28 she's conducted data-driven analysis to
0:30 find and develop solutions for clients
0:33 in both the federal and public sectors
0:36 and dr. hal earned both her PhD and MS
0:38 degrees in transportation engineering
0:41 from the University of California at
0:43 Berkeley before I hand it over to dr.
0:46 Hal a reminder that this webinar is
0:48 being recorded and will be available on
0:51 the MSS BTI website to stream and
0:55 download welcome dr. Hal the floor is yours
0:55 yours
0:59 Thank You Laurie and having hi everyone
1:02 thanks for joining the webinar I'm very
1:05 excited to be here to share some of my
1:07 insights on this really heated topic
1:11 data and analytics and becoming an
1:14 intelligent organization so here's the
1:17 little agenda for today first we're
1:19 going to just talk about what is
1:21 analytics what is an intelligent
1:23 organization and what is really the
1:27 relationship between these two and then
1:29 we're going to do a Mythbusters session
1:31 where we're going to identify some
1:33 common pitfalls on your journey to
1:36 becoming an intelligent organization
1:39 what's the myth versus with the reality
1:43 and lastly we're going to talk about why
1:46 does it matter to me and so some of the
1:49 benefits of analytics and intelligence
1:51 and we're going to conclude with a case
1:55 study so let's jump right into what is
2:01 analytics by definition analytics is a
2:04 discipline that applies logic and
2:07 mathematics to data to provide insight
2:15 so in the word analytics it's not about
2:18 storing or collecting of the data that's
2:22 more of a data engineer job but it is
2:25 really about using the data using it to
2:29 get inside to derive action items and
2:32 therefore to get business values out of
2:34 your data and that all that kind of work
2:39 is more in the realm of the role of a
2:41 data scientist
2:45 so that's analytics what about the
2:47 intelligent no organization wouldn't
2:50 what it means to become an intelligent
2:54 organization so this check work quote I
2:57 think explains it perfectly so being
3:04 intelligent as an organization it's
3:07 really it's really about the
3:10 organization's ability to learn and
3:12 translate that learning into action and
3:16 do it rapidly and that is ultimately
3:19 becoming the organization's competitive
3:22 advantage at its core putting this
3:25 definition in the modern business
3:28 context I think it overlays a lot with
3:32 analytics so putting these two concepts
3:35 together being intelligence really means
3:38 learning from your data making sense of
3:40 your data and then apply all that
3:43 learning into actions and then into
3:47 values so in this way you become
3:50 intelligent and you stay competitive to
3:53 see and one other thing I want to point
3:54 out here is seeing the big names like a
3:58 TV or other big names like Google's and
4:01 Facebook might make you think that
4:03 analytics and being intelligent
4:07 organization is all just a game for the
4:12 industry giants or the tag savvy big
4:15 names but that's not really the case
4:17 it's not just for a game for the big
4:20 firms really there's a solution for
4:22 everyone for
4:26 every company of every size there's a
4:29 solution to become intelligent and be
4:36 better at what the company is doing the
4:38 summarize putting analytics and the
4:40 becoming intelligent together in the
4:44 word intelligent organization has the
4:47 ability to constantly learn from the raw
4:50 data it has and the generating value out
4:53 of it constantly at its core the
4:55 critical engine keeping this the whole
4:58 process going the whole data to value
5:02 process going is the analytics engine so
5:04 what is happening really in this black
5:09 box here let's take a closer look um so
5:12 first let's revisit the definition of
5:14 analytics there are three key components
5:17 in that in this definition and they
5:20 together comprise analytics and that is
5:24 using you have to use your data and that
5:27 means you identify or acquire or combine
5:31 or manage in some way the necessary data
5:35 you need and then it's about using that
5:38 data to provide insight where you
5:40 discover hidden patterns and derive
5:44 business insights from the data and
5:47 finally all this is for the purpose of
5:50 making better decisions or taking
5:54 specific action and a front from this
5:57 from doing this with analytics we arrive
6:02 at your value so in the word endless
6:05 rows of raw data here won't help with
6:07 your business much but you have to
6:09 derive smart insights and it turned them
6:18 so why now data and analytics have been
6:20 around for a very long time
6:24 but why all the heated discussions now
6:27 that is because never before in the
6:30 history really have we had so much data
6:34 and so much processing power to analyze
6:36 the data so on the data
6:39 on the data side we're just having more
6:42 and more data as well as new sources of
6:45 data 90%
6:48 90% of all the data in the world has
6:51 been generated over the last two years
6:55 and on the other hand not only we have
6:58 the data but we have in the increasing
7:01 processing power to handle the data and
7:04 really to analyze that data at the most
7:07 granular level and that means two things
7:10 first the potential business benefits
7:13 when these two combined what they can
7:18 generate is tremendous so using data and
7:21 analytics really can bring you value and
7:25 the second is really it is crucial for
7:28 you as an organization to become
7:32 intelligent about your data because with
7:34 everyone doing it you really have to
7:37 root this insight driven or analytics
7:40 driven strategy within your organization
7:44 it's almost like a must-have for your
7:45 organization in order to stay
7:48 competitive or even relevant in the near
7:53 future so with that said how do we
7:58 become an intelligent organization it is
8:02 actually an integrated journey it's not
8:04 just about the hiring of your data
8:06 scientist and by some expensive
8:10 analytics tools it is a very integrated
8:13 journey that includes a lot of moving
8:16 parts within your organization that
8:20 includes the strategy you're deploying
8:24 and some assessment of where you are at
8:28 the current journey what kind of talent
8:31 you need what structure of the analytics
8:33 team or the governance of your analytics
8:37 and also the approach your how you're
8:39 going to use data how you're going to
8:43 use analytics and all these together
8:52 destination was becoming truly and
8:56 intelligent organization and of course
8:59 this journey is not a journey without
9:02 pitfalls and road bumps so what are some
9:05 of the pitfalls you can run into along
9:08 the journey or so we'll explore a few
9:13 common ones in our next section so talk
9:16 about some of the popular myths that's
9:19 out there and explain why it is a myth
9:23 and what is the reality the first one is
9:28 a really common one that we hear a lot
9:30 people say oh my organization is already
9:33 using analytics you know we summarized
9:35 our data in the reports or dashboards
9:38 and we're getting some insights on our
9:41 past performance so we have analytics
9:49 that is not the case so the rest the
9:51 reality is analytics it's not just about
9:55 looking back at the past it's about
9:58 making you more confident about the
9:59 future helping you better prepare for
10:03 the future by increasing your options in
10:06 the present so like when you are
10:09 something like when you are driving
10:12 using analytics it's not just looking
10:15 into the rearview mirror where you look
10:18 back at what happened behind you but
10:22 it's more about looking at a windshield
10:24 view where you look into the future
10:28 gather information about what's coming
10:31 up on the road and be well-prepared for
10:35 it so this graph explains a lot for
10:44 explains a lot the strategic is called
10:47 strategic intelligence and it's all
10:52 about timing so say if we have an event
10:57 occurring here and this triangle here is
11:01 the range of options that we have
11:04 so before the event occurs if we have
11:08 some information and we use that
11:13 information through some analysis and
11:16 make a decision of what we're going to
11:21 do the range of options for us to take
11:26 action our response range is much wider
11:32 and on the contrary if if we don't get
11:34 any information until after the event
11:37 happens a range of options really begins
11:40 to narrow very quickly and imagine
11:42 you're driving again
11:46 so like this event maybe a car is
11:48 aggressively changing Lane
11:53 ahead of you and if you look if you look
11:56 into your windshield field and you're
11:58 sort of sort of see that see some signs
12:01 of that happening like it's speeding up
12:08 or if blinkers on so that's the data you
12:11 have and you make some judgments using
12:14 your analytics with within your brain
12:16 and you think all this car is going to
12:19 change Lane so you make till then you'll
12:22 have more options to deal with it you
12:25 can either slow down or speed up it's
12:27 not your personality and you can also
12:29 change the other lengths to avoid this
12:33 aggressive driver then if you don't have
12:36 that intelligence if you only get to
12:39 respond after you see the car is
12:43 changing Lane that makes you range of
12:46 options much narrower and you have to
12:52 react with much less confidence so
12:53 you're just in the firefighting mode
12:56 dealing with the event that's already
13:00 happened so all this information that
13:03 you gather ahead of time and you analyze
13:06 to help you help prepare you to take
13:09 actions these are the value added
13:13 through data and through the predictive analytic
13:13 analytic
13:19 that we performed on that data so that's
13:27 our first myth and next myth is for our
13:30 analytics effort I need to start with
13:34 data infrastructure another very common
13:38 is and we hear we actually hear a lot of
13:42 similar comments like we all we need to
13:46 build our data base first or a better
13:49 timing for using analytics is until
13:52 after we have we build our big data
13:56 warehouse project or something like we
13:58 need to collect all the data we have and
14:00 then put them through analytics to see
14:03 what kind of results just bubble up and
14:06 along with those comments we are we are
14:09 hearing real frustrations out there as
14:14 well we have in many senior leaders
14:16 complain that they have been spend
14:19 spending powers of money on their data
14:22 infrastructure projects and find
14:24 themselves sitting on more and more data
14:28 really seeing no value out of that data
14:31 or not knowing how to derive any value
14:32 out of there
14:34 so the question is really why are we
14:37 stuck in forever data collection and
14:41 manipulation and not seeing any value so
14:45 this is a process in a lot of people's
14:48 head about how you go from data to
14:52 insights and as it might seem like a
14:56 smooth progression it's really not it's
15:00 we're seeing is more like a bubble neck
15:04 situation here with a lot of people in
15:07 the data collection and the storing
15:10 process where only a few can pass the
15:15 rule to arrive at inside and those
15:18 getting insights from the data is what
15:21 we call a truly intelligent organization
15:25 and from the insights they get values
15:27 fairly quickly
15:32 so so what is the secret for these few
15:34 intelligent organizations making that
15:40 leap from data from all kinds of efforts
15:44 around data to using the data to
15:48 generate the inside it is actually a
15:51 twist on how to start your analytics
15:56 project in the world you shouldn't you
15:58 should have data at your data should be
16:01 at your service within your control to
16:04 serve your purpose but really not
16:07 letting data to drive the entire process
16:10 from the start
16:13 so here the myth is busted and the
16:17 reality is we think every analytics
16:20 project should really start with asking
16:24 the right business questions remember
16:27 earlier we talked about how you get you
16:31 get from data to insight to value and
16:35 well to start to start this process you
16:37 need a direction for what kind of data
16:40 that you need to be analyzed rather than
16:43 just starting with all the available
16:45 data you have that's more like boiling
16:48 the whole ocean so really there's the
16:51 step zero before all these and that is
16:54 to start with a question to identify a
16:57 key business question to be answered
17:01 with the data a question that's tightly
17:04 tied to your business drivers or your
17:08 largest business pain point and this is
17:10 what I call the question to value
17:13 approach starting with a question that
17:17 sets you on the right track all the way
17:20 to value and all the way to becoming an
17:28 intelligent organization and here's our
17:33 third news which is analytics comes down
17:37 to having the technology again a very
17:40 common one it is common to think and
17:42 Linux is just about having the right
17:45 technology and the right technical
17:48 talent and when you have those you're
17:51 ready you the insights and the business
17:55 values will just come out naturally that
17:58 is really not the case well the reality
18:03 is beside technology your business
18:06 competency and the culture all these
18:10 three are necessary factors for your
18:14 analytics maturity and for a successful
18:18 analytics project so to be to get ready
18:21 for analytics to generate a value to you
18:25 you need all three of these and we
18:28 talked a lot about technology so that
18:31 your data infrastructure data quality
18:34 and your technical talent business
18:37 competency is really about having the
18:39 right leadership having the right
18:44 business decision making process to
18:48 implement analytics it's about asking
18:50 the right business question like we
18:52 talked about and also interpreting the
18:56 analytical results accurately under to
19:00 put them into your business context and
19:04 finally the culture culture is something
19:08 often involves motivating the using of
19:10 the analytic results or fostering the
19:13 willingness to implement the action
19:16 items and it is very critical because
19:19 values are not seen until
19:21 recommendations are implemented into
19:24 your organization's day-to-day
19:28 operations and that's often by employees
19:33 way beyond your analytical talents
19:37 so to summarize here when you ask a
19:39 question am I ready or it's my
19:42 organization ready for analytics think
19:44 beyond the technology you think all the
19:47 three components technology business and
19:51 the culture and always ensure a balance
19:54 the growth among the three
19:57 and that brings us to our final news
20:02 analytics is a set it and forget it this
20:04 is also very common many people
20:07 understand analytics as some operational
20:10 routine that once it's set up you can
20:13 just leave it running data will keep
20:15 flowing through the system and keep
20:21 generating values for you well that's
20:25 not true the reality is your analytics
20:27 plan should always be revisited and
20:32 adjusted to match the changes many
20:35 changes I have and they're both internal
20:38 and external changes let's first talk
20:42 about internal changes as we talked
20:45 about analytics this meant to help with
20:47 your organization's decision making
20:52 process at a very high level so as it
20:55 grows its capability changes and also as
20:58 your organization grows your needs and
21:01 the priorities for analytics will also
21:04 change the old analysis you are doing
21:06 the questions it's answering the
21:09 approaches using they may not be useful
21:11 or even relevant anymore as your
21:15 organization grows so internally
21:18 analytics should always be adjusted to
21:22 match the overall strategy development
21:27 plan of your organization and that's
21:29 internal and the looking externally
21:32 there are also all kinds of changes
21:36 happening and they are happening rapidly
21:39 first let's talk about technology and
21:42 the analytics select technology
21:48 landscape is just ever-changing and the
21:51 weave in a lot of market disruptions and
21:57 there are disruptions coming up soon so
22:00 during the past ten years we've already
22:03 shifted from a really like a coding base
22:05 to platforms where you do a lot of
22:07 coding to analyze
22:11 data to a visual basic platforms that's
22:13 where the modern business intelligence
22:18 comes in and making it a more business
22:21 user-friendly and in the next two to
22:22 five years
22:24 there are many predictions going on
22:27 about this key trend augmented analytics
22:31 where is the power of artificial
22:33 intelligence and the machine learning
22:36 will be leverage that into advanced
22:41 analytics and making it a much much more
22:46 powerful and more usable for business
22:50 users so ten years ago we struggle to
22:52 find any machine learning and they both
22:55 business analytics applications and in
22:57 ten years we'll probably struggle to
23:01 find any that don't do that so this is
23:05 just one example of a technology trend
23:08 and there are many others on the way as
23:11 well so these should all be taken into
23:15 consideration when you are monitoring
23:18 and adjusting your analytics strategy
23:21 just to make sure your organization is
23:23 keeping up with all the technology
23:28 trends the the other external factor
23:30 that's ever-changing is technical
23:36 technical talent so a few years ago the
23:39 robot data scientists didn't even really
23:42 exist and even today the definition and
23:44 the responsibilities of a data scientist
23:48 it's rapidly evolving and leading to new
23:52 possibilities for analytics role within
23:55 your organization so that's definitely
24:00 something to always keep an eye out one
24:04 teacher and we observe in the talent in
24:08 the talent is that the boundary between
24:10 technical and the business talent is
24:13 getting blurred so it's important to
24:16 find your your own mix of the right
24:19 balance of technical and business talent
24:21 so we have
24:24 analytics skill and a bottom where your
24:28 typical data scientist resides and we
24:30 have these business skills up here and
24:33 they are these two skills are reading
24:35 really getting merged so as the business
24:40 analyst within your organization doing
24:43 basic analysis for you is that
24:46 incorporated with some analytics do we
24:50 have citizens data scientists where you
24:52 do analytics for your day-to-day job on
24:55 a daily basis and even though there
24:56 aren't that many today
25:00 gartner predicts that in less than five
25:03 years the number of citizen
25:06 data centers will grow five times faster
25:10 than those highly skilled data scientist
25:13 roles and on the other side as a
25:14 business leader
25:17 you also need some analytic skills to
25:22 understand to understand data and
25:24 understand the inside and then that is a
25:28 role called analytics translator where
25:31 you really have those business leader
25:34 skills but some knowledge of analytics
25:37 as well so it's almost non-existent
25:41 today less in less than 10 years
25:44 McKinsey is predicting that the demand
25:47 for analytics translators in the US
25:52 alone may reach two to four millions so
25:56 these are really key signs to look out
25:58 for and when we are talking about
26:01 technical talent and definitely always
26:05 adjust your analytics plan to make sure
26:08 that they match all these changes okay
26:12 so we've covered what it is and busted
26:15 some myths and now is the our final
26:19 section we talked about why doesn't
26:22 matter to me so is there an analytics
26:25 really working and why should I care
26:30 okay so let's talk about it is there a
26:33 value really from analytics well the
26:34 short answer is
26:38 according to Harvard Business Review
26:42 companies are seeing five to six percent
26:48 consistently higher productivity rate
26:49 and profitability when they are
27:00 also we have all these numbers reported
27:05 as the gain from analytics from
27:07 different aspects so we are seeing
27:11 increase in productivity a higher
27:14 customer retention also are just overall
27:19 a higher profit margin per customer
27:29 besides all that advanced analytics
27:34 users are also just gaining more
27:38 insights from their customers
27:43 that's one big that's one big use case
27:47 there because customers per day today
27:49 are just becoming more and more
27:51 demanding and expecting personalized
27:54 services and using analytics do make a
27:57 difference there in telling what the
28:00 customer really needs and what they what
28:02 they always respond on the 2/3 and X
28:06 marketing efforts it's not just facing
28:10 the customer but also analytics can help
28:12 on the operational side of the business
28:14 where it is able to identify
28:17 inefficiencies in the organization and
28:22 provide data driven solutions so in a
28:25 word insights from advanced analytics
28:28 are really disrupting everything from
28:30 your customers to your internal
28:37 operations to just everything and as we
28:40 talked before it's not just a game for
28:43 industry giants or unicorns
28:49 is for everyone and according to our
28:52 Gartner survey on the MSE the mid-sized
28:58 enterprises more than 65% of the
29:02 companies interviewed reported they get
29:05 better business outcomes by increasing
29:11 their analytics and bi adoption and like
29:14 as we talked about in fact there is an
29:17 analytic solution for everyone you're
29:21 not competing with those big names or
29:26 some mums just very techie companies
29:28 you're not competing with them on the
29:31 same playground it is really all about
29:34 finding a solution that best suits your
29:36 own business model and answer your own
29:39 business question to improve upon
29:42 yourself so if it has to be a
29:44 competition it's a competition with
29:47 yourself you're evolving into a more
29:50 intelligent more analytics driven
29:53 version of yourself and you're not
30:03 so having understood that looking within
30:06 the organization where does analytics
30:11 have an impact oh well just everywhere
30:16 and it impacts everything having a great
30:18 analytics approach really penetrates
30:20 into all the key aspects of an
30:24 organization and will own it on its own
30:27 keep finding new ways to help these are
30:33 all the aspects that it might it is able
30:35 to provide solutions we've covered
30:38 customers which is definitely a big part
30:42 but it is easily transferred into
30:45 helping with your marketing effort and
30:47 also helping with your strategy and
30:49 vision and the plans and the expansion
30:52 plan and looking inside and it also
30:55 helped with increasing operational efficiency
30:57 efficiency
31:02 helping helping your performance and
31:05 also even employee engagement or HR
31:09 effort so really it impacts everything
31:13 what about industries what kind of
31:19 industry our are being impacted well
31:21 again the answer is almost every
31:25 industry on the face of ours and again
31:29 whether it's like a has a heavy customer
31:34 facing nature like a retail hospitality
31:38 or if you have a heavy operational side
31:40 of the business like you have an
31:41 infrastructure or
31:44 supply chain to manage data and the
31:46 analytics is in each and every one of
31:56 so having understand all all that
32:01 benefits finally we are seeing a case
32:04 study where the benefits are becoming
32:07 real through this one we'll see how data
32:10 and analytics are leveraged to help this
32:12 company to answer a specific question
32:16 and the help the res values from the
32:21 data for for the company so our company
32:28 is an auto aftermarket company where its
32:32 business model is it has a very large
32:35 marketing spend across their digital
32:37 channels so they do a lot of marketing
32:40 online but none of the purchases are
32:42 happening online all their revenue has
32:45 an in-store so they are see they're
32:47 seeing this enormous gap between their
32:50 online advertising and offline purchase
32:53 so they feel their online advertising
32:58 efforts are have little directions or
33:01 guidance as to what what are effective
33:04 and or not whether or not so the
33:07 question they have really is what kind
33:09 of digital touch points have
33:13 like what kind of marketing what kind of
33:16 online activities are likely to lead to
33:19 which offline purchases and by how much
33:22 by understanding this they are better
33:25 equipped to develop their online
33:30 marketing and campaign your effort so
33:33 with that question in mind that the data
33:36 they need is really to merge more
33:38 transaction data with online digital
33:43 data by some by certain way of merging
33:47 the data and through doing a analysis on
33:49 the data in this case a purchase intent
33:54 analysis specifically they quickly get
33:56 to some insights and distance outcomes
34:00 so what they find a few actions to pay
34:03 attention to include when a customer
34:06 sign up for an email newsletter or they
34:08 are applying for new financing or credit
34:11 cards that's a highly predictive of a
34:16 big service or big purchase happening in
34:20 the near future so they develop the
34:22 strategy to target these customers more
34:27 approached them more to have them come
34:30 to their business and by doing that by
34:33 utilizing that insights from data and
34:37 taking actions they saw a 67 percent
34:39 lift in their overall customer lifetime
34:44 value and they did it fairly quickly so
34:47 this is a real case of how our business
34:51 is able to start with a question using a
34:54 pulling the right data applying
34:56 analytics to the right insight and
35:08 so as we approach as we get into the end
35:11 they are probably getting excited all
35:14 about analytics and just having this
35:17 question I am ready and how do I get started
35:18 started
35:23 well our our recommendations
35:27 always start think big and start small
35:30 so thinking big really means that you
35:31 always think of the overarching
35:34 analytics strategy the approach you're
35:37 taking the business process and always
35:40 balancing technology and culture and
35:42 making them align to your overall
35:48 strategy so that's all thinking big if
35:50 you want to start start small
35:54 remember that analytics is not about the
35:57 entire transformation of your data in
35:58 structure but it's about understanding
36:02 and using the data to create value and
36:06 often to start in a quick and dirty way
36:10 so I really start with a question
36:12 start with a question and a pool of
36:16 small data set do some quick analysis to
36:18 see results quickly and approve the
36:22 value and from there your analytics is
36:25 seeded and then will keep growing and
36:28 most importantly don't wait to start
36:36 small and start today so that's pretty
36:38 much our webinar today to summarize
36:42 quickly today we talked about first what
36:44 it means to become intelligence it's
36:48 about using data to get insights and to
36:52 taking better actions and we cleared
36:56 some news along the way so we know that
37:01 it's really about the future and always
37:03 start with a business question and
37:05 analytics it's not just about technology
37:09 and you need to look out for changes and
37:12 make adjustments and the family the
37:15 benefits we we saw a lot of real values
37:18 realized across different industries and
37:23 for companies all sizes and our parking
37:26 words is really to start to start small
37:31 and start today so that concludes our
37:34 webinar today are very interested to
37:36 learn more we have
37:39 created resources on our ms MSS PGI
37:43 website there are a good collection of
37:46 articles about analytics and many other
37:49 topics that you could feel free to
37:52 explore and we are also hosting this
37:56 workshop in October where where we all
37:58 just come in for a much deeper
38:00 discussion around how to become
38:04 intelligent organization and I'm very
38:06 excited about the workshop it will be
38:10 very interactive hands-on and just a lot
38:13 of fun so check it out on our website
38:20 and stay tuned with that thank you thank
38:23 you for your time and I'm happy to take
38:28 any questions and if you have more
38:29 questions or need more information
38:34 here's our contact information thank you
38:35 thank you very much
38:38 that was excellent great information and
38:41 I'm still reeling from the statistic
38:43 that you gave us that 90% of the
38:45 available data was has been generated in
38:49 the last two years that is an amazing
38:51 idea to deal with
38:54 that's huge while you were presenting we
38:55 did have a couple of questions that
38:58 popped up on the screen that I was
39:02 monitoring one of them is you talked
39:04 about how it's crucial to start with a
39:06 question but how do you come up with the
39:11 right question to ask well that's that's
39:13 a great question
39:18 and it is actually takes a bit more
39:22 effort probably than you think it's a -
39:25 mediator flow essentially a collaborate
39:28 process to come up with a read question
39:32 to ask and that's requiring both your
39:34 analytics input from both your analytics
39:37 team and your business team so your
39:39 business team should be driving the
39:43 process with their understanding of
39:47 their of the business model they'll be
39:49 able to identify the opportunity
39:52 for further improvement or identify some
39:55 of the pressing issues that needs to be
39:58 solved soon within the organization so
40:01 from there and they might compile a list
40:06 of questions to get to ask and then
40:09 that's when your analytics team comes in
40:11 and they're they're here to provide
40:14 insights on whether the question is
40:17 suitable for analytics whether it's a
40:23 good candidate to be modeled some
40:25 questions are some questions simply
40:27 aren't suitable for mathematical
40:31 modeling so they will just make the
40:33 decision and pick the right one so with
40:36 this collaboration will be able to reach
40:38 your question that's critical to the
40:40 business and it's also a good candidate
40:45 for analytics and another thing is [Music]
40:46 [Music]
40:49 really when you're doing this and keep
40:50 asking good questions
40:53 soon you will reach a point where you
40:56 have a long list of questions waiting to
41:00 be answered at this point it will be a
41:02 good exercise to prioritize these
41:06 questions and really manage your supply
41:10 and demand for analytics how to
41:14 prioritize these largely depends on your
41:17 business judgment but to start you
41:20 should trade off factors like the value
41:22 of the question the feasibility and the
41:25 resources required to answer the
41:28 question great it's great information
41:30 sounds like it's a combination or a
41:32 partnership between the business side of
41:36 the house and the data science exam
41:40 title your house one more thing that we
41:42 had pop up can you talk more about why
41:44 culture makes a big difference in
41:50 analytics yes definitely [Music]
41:58 too many people culture is something
42:01 that you think oh I'll get to that after
42:05 the real work is done but culture is
42:07 some is the real thing and it should be
42:10 taken care of throughout the development
42:13 of your analytical effort or really any
42:17 effort you have that is because the key
42:21 component for an initiative to succeed
42:23 is communication and taking actions and
42:29 those more often involve people beyond
42:31 much beyond your technical team who does
42:35 not necessarily lis speak the technical
42:39 language so fostering the culture really
42:41 means having the right way to
42:44 communicate to them and build their
42:46 confidence build their willingness to
42:50 act upon the inside great I think we
42:53 have time for one more question what are
42:57 best practices for data collection
42:59 that's a great question that's a great
43:05 question um I think it's really the
43:09 first again on your we talked about how
43:13 different companies of different sizes
43:19 might take the journey differently so
43:23 how you collect data is definitely
43:24 different you have different the
43:27 resources to leverage and to start to
43:32 start I would say always look within
43:34 internally first looking at what kind of
43:37 data we have available what kind of
43:42 format are they in and think about how
43:45 to if we have the data but it's in the
43:48 messy form how do we transform that data
43:53 into useful formats and we after looking
43:55 internally looking at what data you
43:58 already have then you start thinking Oh
44:00 what kind of data that we might need
44:02 that we're not collecting and that's
44:06 when you start looking for collecting
44:10 new data from either within your up
44:13 or even look for external there are all
44:16 kinds of businesses out there that self
44:21 data as a product and that's also
44:23 something to explore externally as well
44:26 great well thank you very much thank you
44:29 for your insights thank you for sharing
44:31 your your deep knowledge and expertise
44:34 of data analytics we appreciate them uh
44:37 sure and before we sign off I just want
44:40 to remind everybody to please visit MSS
44:43 BTI dot-com where you can get more
44:46 information and register for dr. house
44:50 workshop on october 18th and 19th and
