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hello and welcome to this webinar on BI modernization with looker and bitwise my name is Nathan Nichols head of partnerships here at bitwise we have a packed agenda for you today covering introductions to bitwise and looker followed by recommendation for bi modernization and real-world looker implementation case study we'll also have time for Q&A at the end of the session so feel free to send your questions via the question feature or contact us at any time at sales eff it wise global comm joining me today our two presenters well qualified to talk about bi modernization sign injection a bi solution architect at bitwise brings over eight years of extensive experience in designing and developing BI solutions and delivering end-to-end bi implementations built pencak is a regional sales director with well over four years at looker in a demonstrated history in the computer software industry before I pass things over to Bill and Donna Jay let me first provide a quick introduction to bitwise bitwise the global technology consulting and services company uses over 23 years of enterprise data management experience to help bridge gaps between traditional EDW and cutting-edge technologies including big data and cloud what truly sets bit-wise apart is our technology leadership in creating unique accelerators and frameworks that reduce the time complexity and cost of large-scale initiatives such as BI modernization and cloud migrations founded in 1996 bitwise is headquartered in Chicago Illinois with our UK office in London and global delivery centres in Pune India is a technology solutions provider we help our clients leverage data to enable business insight and maximize their competitive advantage to this end we've partnered with the leading data and analytics companies including Google cloud kale and snowflake and of course looker and with that I'd like to pass things over to Bill to provide an introduction to looker bill thanks Jason Nathan I appreciate that so guys what I'd like to do is just initially cover looker as a company in our value proposition in the market I'll also touch on the architecture of our looker data platform to give the audience the ability to see you know how looker works behind the scenes so look it was founded about seven years ago and our founder Lloyd tab had a vision that you know the market would shift we would shift from the likes of a Teradata an Oracle sequel server databases to more faster column or data stores such as Google bigquery Amazon redshift and snowflake and that pet has paid off right so what you see today in the marketplace is these very very performant and fast data stores and looker as a technology takes advantage of those data stores and I'll I'll get into more detail around the architecture and how that works when you think about the analytics industry and I spent the number of years at Business Objects 20 to 25 years ago Business Objects MicroStrategy and Cognos were the first providers of analytics outside outside of Excel right and you know those companies laid the tenants of some really strong architecture and then as we evolved about 10 to 15 years ago clicking that blow came onto the marketplace and developed a very visual user friendly Excel light technology which took the market - you know another innovation and really the end user was like that because it was very Excel like and the visualizations were very good so we feel that today lookers positioned to be that third wave business intelligence analytics data platform vendor to take advantage of what's happening in the market marketplace with these data Lakes as you can see you know we're looking to power data democracy with over 2,000 customers 500 developers and as you can see here on the bottom looker is data agnostic as well as cloud agnostic so we support all these different data stores as well as the different cloud providers so as I mentioned really our architecture takes advantage of these new column or databases and what we have is in database architecture so many people might hear that terminology and said well what is that how does that make look are different so really what it does it eliminates latency at the at the end of it if you took anything away from our architecture is that the looker is gonna stay in database making sure that when the end user clicks refresh on their dashboard that they're getting up-to-the-minute up to the second data as you can imagine in today's day and age with a very competitive landscape business users need to make decisions in a very short period of time so they can't have the data going out into a cube of data where there's latency and they can't have it going out into memory which some of our competitors have done so getting back to my earliest remarks about looker being positioned for that third wave of business intelligence that makes looker suited for that and then the next thing is our semantic layer now the ability to have a single source of the truth to have all the end users working off of the same measures and dimensions just makes it that much easier for the end-user to complete that data and then lastly which we'll talk about in the in our youth business you case is you know API extensibility looker takes 99% of our product and will allow you to utilize that into a JavaScript application into a portal so that you can now have looker as part of your environment for your customers or for your internal internal users but it really sets looker up to be that very user friendly what we call powered by looker a solution so is this just talks about you know our architecture in a little bit more detail as you can see here you know when you think about most companies they have production applications that are coming from a myriad of databases and what we say or profess as a company is just extract and load centrally and then looker will do the transformation at query time so as long as the data is put into that centralized data Lake and what you find in today's climate is that these companies or most companies are putting it into those databases that I mentioned in the purple layer what you find is the ability for us to create that that that's that semantic layer or that single source of the truth and then up above it gives you the different workflows you know that you can can tailor to the use case so as you can see here Home Depot is using it to replace Splunk you know from a security standpoint they're running 120 thousand events on looker to notify them if there's any breach in their in their security Comcast is using it to infuse workflows with data into their different systems to allow end users to interact with the data in applications that they are using on a daily basis so this kind of gives you some examples of how other companies are utilizing look and then this talks to the value proposition again around the looker data architecture and it allows companies as well as users to do so many different things with looker as a data platform you know one of the things that I feel personally about or strongly about is the fact that you know an end user gets data they're always gonna want to ask that next wide question so when you see this modern self-service bi it allows that end-user to now have the dashboard as a starting point not as an endpoint and really what that means is that they can now drill and filter on that data very easily within the looker interface but just say that they say this is great I understand why we're making money in this region but I don't just want to understand some more details around that but the data is not there so instead of going out and asking a more technical IT person to get them more data they can do it themselves via an ad hoc analysis and what drives that is our semantic layer the ability for that end user to point and click on that measure or dimension looker creates the sequel behind the scenes brings the data back into their dashboard and now within a couple of clicks they're just more productive the other thing that I'd like to point out too is the ability to have near real-time alerts so you know our scheduling mechanism allows customers to set up thresholds based on a certain value and if that value falls below or above a certain metric automatically have looker notify an individual and/or a group that something has changed and that could be put into a form of a text message it could be an email it could be a number of different things that alert that that that end-user or that group of folks so as you can see here is looker as a data platform we allow many aspects of infusing data to the end users in different formats into applications into an email into a text put it into it into a slack and into a slack message so a lot of different ways to notify that end user thank you bill um the repetition of common VI problems might be motivate businesses to lose and questioned the value of business intelligence before we dive into these problems I would like to thank each and every one of you for joining us today for this presentation thank you I'm sure you'll find it useful so what are the common challenges we may face in bi modernization identifying these challenges is really important first step in formulating some bi strategy let's take a look into some of these challenges please note that this are not all inclusive list there may be other challenges which would be specific to the industry and the business domain so the first and most common challenge would be the data varieties nowadays industries find it difficult to access the right data at the right time and even if they do find what they're looking for data formats are typically so complex and unstructured it's hard to aggregate that data in to be consumed by the way iTune not considering the data varieties up front may set up your bi strategy to fail once the data righties are identified next comes the data integration getting this data from these various sources and putting it into the format that is consumable for bi platform is the next problem to solve getting this data timely manner is also an important business aspect as processing data in real time facilitates prompt decision making the next challenge is the skill and effort the skill and if that is required to make you for bi strategy a reality many times companies might have a well-articulated requirement a sound V a strategy and a good tool solution but lack of technical skill like designing building maintaining and supporting their application this results in B application to run slowly break frequently deliver uncertain results and eventually leading to rising cost of using the BI solution the causes of lack of execution often are multiple and varied as are its remedies the data security is at another concern that has to be addressed security of data at rest as well as prevention of unauthorized exposure is a very important aspect of securing business assets your BS strategy should blend well with the elder price security considerations and should no way compromise the data security the next challenge would be the scalability the scalability from functional as well as non functional aspect it's something that we should be considering the functional scalability consists of how hard to add new features to your platform the non-functional aspect of it which is also our next challenge is the performance how well a system behaves under a load and latency in a system defines its usability by different stakeholders gone are the days when executives used to wait for days to get the report they requested it is a generation of the sub service where the information is expected in the real time though all of us know that how important bi and the analytics is in decision-making and the growth of our organization it may not be primary revenue generating stream for most of the organization and so the business may not be keen on spending a lot on these kind of systems that's where the cost control comes into the picture getting maximum amount of bi platform while keeping cross flow is a key challenge to solve and the last but not least is the installation and the deployment in today's agile word time-to-market has become a key element in successful product getting system up and running quickly is a goal of my organization which heavily relies on the BN strategy apart from getting it up and running deploying incremental changes are also important to get the new functionalities to the production and make it live for the users so with this dis challenges let's let's now talk about some of the considerations to successfully tackle this common challenges the consideration again the consider consideration that mentioned here are the general guidelines and those might needs to be tricked based upon the business needs so the consideration in incorporating the data for variety of sources would contain the different formats of data from various sources structured vs. unstructured data melting together this data from various sources so that it can be used by bi platform different data storage options the creation of an integrated platform that can store huge volume of data while organizing and analyzing it to your specifications so all those things needs to be considered when we are talking about varieties of the data once the data sources are identified and the data storage options are decided it is time to think about the data integration strategy data pipelines could play with a little role in data integration a bi solution which could be loaded with automatic ETL capabilities to process data sets that needs to be restructure will be a real solution here this will enable users to create a single source as well as front-end with data visualization capabilities ideally the back end of the solution would be the manipulate the data for it to be analyzed in the front end also the ability ability to capture a process and data from multiple sources in real-time facilitates the prom decision-making the next one is the pollak got data persistence and it may need to be considered if data from various sources cannot be aggregated in single data storage option makes our data storage technologists need to be used in that case and your BI platform should be able to work against all these data storage options effectively the next one is the embedded analytics but I will skip it for now as we are going to discuss it in brief later in this presentation lots of organization may not be satisfied with the BI system they already have if the executives in organizations are still using an excellence 10 solely to get insight from their BI system then that is the indication of the defect deficiency they have in their beer system a good practice would be to replace excel sheet with intuitive dashboards to make data more engaging meaningful and eventually very powerful hence for this bi solution should provide the ability ability to create add wines filters and calculation all without coding a self-service business intelligence solution enables execute use to create a customized report in no time with little involvement of IT was the entire solution is implemented in data security selection of storage options and BI tools play a vital role tried to use out-of-the-box security features from VI tools before turning to the custom security implementation data encryption when they die creeps on to be used try to use the well-known encryption algorithm from the data storage tools itself for the system scalability the things to consider would be scalable architecture and scalable environments the scalable architecture makes sure to factor in all possible business needs a friend so that they are very less changes afterworld try to follow open and close principle meaning system should be open for extensions but closed for all the modifications with our ability of various cloud providers scalable environment has become the reality for most of the organizations now system can now be designed to virtually scale to infinity in vertical as well as horizontal manner we discussed earlier as to how the embedded analytics could be helpful in satisfying your bi needs hybrid approach is what enables you to implement the embedded analytics you could take advantage of unique features from various tools languages and platforms and combine them together to create your own homogeneous platform the selection of BI tools and technologies is going to determine your development pace certain selection would need need very high time upfront but once it is up and live adding new features would be queue and fast other selection could you could give you quick to market benefit up front but any new feature may take a considerable amount of time choice would be yours with the reunion rapid development or quit to market benefit and the last but not the least would be the deployment an organization as organizations are adapting to agile software development methodologies day off strategy has become as important as your software development strategy or as I always say DevOps must be coherent part of overall system strategy selection of BI tools impact your day offs process as much as it impacts your development process with the help of right tools they love strategy and cloud technology you can destroy and rebuild your bi platforms in minute making deployment a piece of cake so about you let's take a look at the cake buddy yeah so what I'd like to do is just touch on this customer that bit-wise looker as well as Google partnered to really create a fantastic platform for their customers so this is a customer that is based in Atlanta Georgia one of the largest credit-card processors in the world with about 2.6 million customers worldwide and where they started was from a legacy application that really was somewhat static in nature with the data and did not have an up to the date front-end if you will so this company went through a very extensive process of identifying technologies that would make this state-of-the-art modern data stack work for their customers so they did a cloud evaluation they did a database evaluation as well as analytics and when you kind of look at the sheer scale of this it was petabytes of data right if we talk about you know migrating nine billion rows of data into bigquery it was it was a massive undertaking and they had to choose technologies that would scale to the data size as well as how many users would be accessing the system so in essence what they built was this API driven engine that would allow looker to be the analytics provider to this JavaScript application and this application was being mutable you know by their customers to look at transactions over time price demographic and really what they the reason why there had a product initiated this was to differentiate them as a as a provider in the very competitive marketplace that they're in so their customers you the data as I mentioned earlier look her strong suit is the ability for end-users to now get that data and now drill and filter on that so where they came from this company where their end users were just getting static data now it's it was interactive and allowing them to now drill and filter that and all controlled by a security layer that only allows each of their customers to see what the individual role of that person accessing the data should see so they gave them the ability to empower their customers and this has taken the company to 2 to the next level and has been been a huge success so here's just a little bit more details around that but you know when you look at what looker provided for this application it was that kind of single source of the truth that I spoke of where all the end-users are seeing business terms that they're familiar with allowing them to have that security and then also to be able to do this all through an API layer that would allow the company to feed looker into this this JavaScript application and not utilizing our front-end so all our analytics was driven through the application and this application is utilized for many different aspects of their business but allowed looker to be part of that the analytics provider to again allow those end-users to slice and dice that data to get answers to their questions so this is the technical architecture for the system that we'll just explained this is the the embedded analytics platform that was created for the payment processing company and bitwise and look at how to build this architecture as bill mentioned the looker was selected as a bi tool after the careful review of the multiple cloud strategy and the evaluation to support the visualization and the self-service capabilities required for reporting business intelligence analytics and other payment processing activities needed for merchant account maintenance and transaction processing so some of the scenarios that that that needs to be considered that that was considered while coming up with this architecture was the large volume of data which which was spread across the globe the started decision to migrate all on-premises applications to the Google Cloud though unpromising application was developed in wave focus which they wanted to move away and get into the the BI platform is which is more intuitive in addition to this scenarios there were some problems that to be solved the higher cost on our demise of platform was one of the problem the performance issue and the limited UI UX features that hindered the user experience from legacy tool was another one a client required a reporting tool and the visualization to that field within the GCP cloud strategy the solution needed to meet the stringent business timelines for the migration and the last one was to facilitate the cultural change among the end-users adapting to the new cloud-based application so if you see this architecture based upon the considerations that we just discussed it takes data from various sources throughout the organization and the data was ranging from mainframe files to the Oracle databases the sequel databases on premises databases to the natives our data like so all this data has to be brought onto the Google cloud platform Google bigquery was decided to be the data Lake hosting storage option along with the Google storage bucket and and the cloud sequel the micro-services was designed to interface between the branded application that was developed in the react.js and the mic the the API layer which we just talked about the micro-services would interact with lucre and provide the required data through the local api's to the randall portal on the front end so a couple of things that I would like to highlight that lucre provided out of the box which was very important in successful of this architecture and the first thing was out of the box highly interactive data visualization option on top of that they also provided the ability to extend those visualization using the custom JavaScript libraries and that and the second feature that was very important in making this architecture successful was the out-of-the-box APA APA Kappa bilities so if you would like to take it a technical deep dive into the similar architecture please feel free to contact us on the details provided later in this presentation this is the the de visualizations that you are seeing on the screen right now this is the sneak peek into the platform developed using the architecture we just briefly discussed these are the data visualizations along with the branded reports which are proud through the custom built micro services on the api's the key result from the implementation of that architecture was the improved performance on the reports and dashboard by 30% using the lucre api's we also were able to achieve the data as a service model with local data group component data base rates were reduced by up to the 40% resulting into the enhanced user experience the second key result was enhanced decision-making in which real-time data insights and interactive data visualizations while handling the large volume of data a help user and the business to make the effective business decisions it also gave the greater flexibility for end-users and business analysts to directly explore the data and create on-the-fly report and visualizations with no sequel knowledge and the last one is the controlled cost because all this infrastructure was cloud hosted there was no cost in maintaining the unpromising structure and unlike other reporting tools there was no cost in running this running the development of the lucre software then the the development of the looker is fully web-based application which you can start right into the web browser and take it from there there is no new or additional software file manager so to wrap things up here as you can see we've got the expertise within an n data lifecycle and data movement with the right partnerships in the bi stick to provide a complete modern bi solution if you're looking at exploring use cases or evaluating tools or need help with their strategy and roadmap fit wise and looker can help contact us sails at fit wise global comm then we can explore strategy assessment options or get a deep dive into the case study presented so there are multiple questions coming in for the question session let's quickly get through a couple any that we don't get to we can certainly follow up directly and get you the details that you need before I do move into the Q&A let me thank everyone for joining us today thank you for taking time out for the session thanks Donna Jay and bill for your presentation so the first question I think this is directed toward Zidane and Jay what are the things to consider while selecting a bi tool that's really good question so it really depends upon the kind of platform you are trying to build if I have been us to design the BI platform for the embedded analytics the things that I would look into would be the out-of-the-box API support the authentication and authorization support and its extensibility the data which I villages and capabilities like drill through slice and slice and dice etc localization support if you have end users from different language wrists and the think dedicated technical support from the bi tool you will say why is that so important the dedicated supports is important because whenever you wanna start implementing your system you are going to come across the technical roadblocks having dedicated support helps a lot in this case you need a support team which works with you and not just for you okay and then bill I think this can be our last question and then we can wrap up how long has Google bigquery from GCB been partnering with liquor even prior to the recent acquisition thanks Nathan the relationship has been pretty strong for for a number of years as I mentioned our founder of Lloyd Tabb was actually on the Google's what they call leaders board working as some of the brightest minds at Google for years about you know what we were doing so years ago we partnered I think it was when I joined about four-and-a-half years ago Google became a partner and strategically after that you know we partnered with with other vendors in the data based market and technology leaders and that partnership has worked well because within Google we were able to support you know the native sequel on bigquery we were able to support nested queries so when you think about companies making that investment in bigquery that's what they want to hear from their bi vendor so that was such an easy story to tell when we went on site and that example we just gave about one of our customers our joint customers with bit wiser and looker that was a big part of why I think we we won that that opportunity but you know what have you seen from a transition as we were purchased by Google the things that are coming down the pike are really exciting because you know they have bi engine which is there in memory cache layer that Google's looker is going to be supporting their machine learning their ability to do natural language processing so the future is bright with the partnership that started over four years ago and with Lloyd Tab having that executive level and technical level relations has has really enabled us to to work really well with them so yeah so that in general it's it's been a fantastic relationship and speaking for my colleagues we're super excited to be now part of the Google cloud platform fit stuff thanks Bill and thanks down in Jay with that we will conclude for today as I mentioned if we did not get to your question we will reach out directly and even beyond this session can always reach out to us at sales at bitwise global.com and with that have a great day thanks for your time and thank you everyone you you