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Cloud Scale Analytics Pitch Deck
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Overview of Cloud Scale Analytics
Nicholas VossburgFolgen
Senior Product Marketing Manager um MicrosoftAnzeige
Cloud Scale Analytics Pitch Deck
- Cloud scale analytics Author name Date
- © Microsoft Corporation The world is changing
- © Microsoft Corporation Today, 80% of organizations adopt cloud-first strategies AI investment increased by 300% in 2017 Data will grow to 44 ZB in 2020 (1 ZB = 1 trillion GB)
- Today, 80% of organizations adopt cloud-first strategies AI investment increased by 300% in 2017 Data will grow to 44 ZB in 2020 (1 ZB = 1 trillion GB) Cloud AIData
- © Microsoft Corporation Cloud Data AI Organizations that harness data, cloud, and AI outperform
- © Microsoft Corporation Organizations that harness data, cloud, and AI outperform Nearly double operating margin $100M in additional operating income
- © Microsoft Corporation There are barriers to getting value from data Complexity of solutions Performance constraints Rising costsIncongruent data types Data silos
- © Microsoft Corporation Derive real value from your data One hub for all data Support for diverse types of data Unlimited data scale Familiar tools and ecosystem Lower TCO On-premises, hybrid, Azure
- © Microsoft Corporation Advanced Analytics Social LOB Graph IoT Image CRM Cloud Ingest Store Prep & train Model & serve Data orchestration and monitoring Big Data store Analytics engines Data warehouse BI + Reporting Real Time Analytics Big Data and data warehouse
- © Microsoft Corporation Customer satisfaction significantly improved Transform customer experience Challenge Serve customers better by analyzing usage data at massive scale with sustained inputs above one million events per second and 500GB/day Impact Reporting time for customer usage telemetry goes from 4 weeks to hours Development time cut 80% for shorter time-to-market, reduced costs, and improved responsiveness Fixed capacity and limited insights Complete data visibility for less AfterBefore
- © Microsoft Corporation Seamlessly compatible across Microsoft, and 3rd party BI and data integration tools Cloud scale analytics Accelerate time to market Productive Evolve with your needs Hybrid Get insights on any data Intelligent Ensure peace of mind Trusted
- © Microsoft Corporation Rockwell cut development time by 80% for shorter time-to-market, reduced costs, and improved customer responsiveness Best in class support for SQL, Apache Spark, and Apache Hadoop, and large developer community support Provision your data warehouse and spark environment in minutes with a single click Accelerate your data integration with consistent 30+ native data connectors Empower your data scientists, data engineers, and business analysts to use the tools and languages of their choice Productive
- © Microsoft Corporation Hybrid solution predicts onboard water usage saving $200k/ship/year Leverage SQL Server’s proven performance and security consistently, in the public or private cloud Reduce cost and complexity of managing your existing data transformations by consistently running SQL Server Integration Service packages in Azure Enable consistent user experience with a common identity across on-premises and Azure Hybrid
- © Microsoft Corporation Build and deploy machine learning models on premises, in the cloud, or on the edge Leverage the data science tool of your choice with support for the best of Microsoft and open source innovation Gain insights right from your database with in- database analytics Easily distribute insights across your organization through rich integration with Power BI and other leading BI tools ASOS delivers 13 million personalized experiences with 33 orders per second Intelligent ASOS delivers 13 million personalized experiences with 33 orders per second
- © Microsoft Corporation GE Healthcare lowers operating costs by moving customer analytics to HIPAA-complaint cloud services Built-in advanced security features including Encryption, Audit, Threat Detection, Azure Active Directory, and VNET 50+ industry and geographical compliances Globally available across 42 regions to keep your data where your users are In-depth data governance and access control Financially backed SLAs to ensure peace of mind Trusted
- © Microsoft Corporation Rich partner network Trusted ecosystem to accelerate time to value System integrationData integration BI & analytics
- © Microsoft Corporation Implementing common customer patterns
- © Microsoft Corporation Advanced Analytics Social LOB Graph IoT Image CRM Cloud Ingest Store Prep & train Model & serve Data orchestration and monitoring Big Data store Analytics engines Data warehouse BI + Reporting Real Time Analytics Big Data and data warehouse Azure Data Factory Azure Blob storage Azure Data Lake Store Azure Databricks Azure HDInsight Azure Data Lake Analytics Azure SQL Data Warehouse Azure Analysis Services
- © Microsoft Corporation Ingest Store Prep & train Model & serve Modern data warehouse Azure Blob Storage Logs (unstructured) Azure Data Factory Azure Databricks Microsoft Azure also supports other Big Data services like Azure HDInsight and Azure Data Lake to allow customers to tailor the above architecture to meet their unique needs. Media (unstructured) Files (unstructured) PolyBase Business/custom apps (structured) Azure SQL Data Warehouse Azure Analysis Services Power BI
- © Microsoft Corporation Advanced analytics on Big Data Ingest Store Prep & train Model & serve Cosmos DB Business/custom apps (structured) Files (unstructured) Media (unstructured) Logs (unstructured) Azure Blob StorageAzure Data Factory Azure SQL Data Warehouse Azure Analysis Services Power BI PolyBase SparkR Azure Databricks Microsoft Azure also supports other Big Data services like Azure HDInsight, Azure Machine Learning and Azure Data Lake to allow customers to tailor the above architecture to meet their unique needs. Apps
- © Microsoft Corporation Ingest Store Prep & train Model & serve Real time analytics Sensors and IoT (unstructured) Apache Kafka for HDInsight Cosmos DB Files (unstructured) Media (unstructured) Logs (unstructured) Azure Blob StorageAzure Data Factory Azure Databricks Real-time apps Business/custom apps (structured) Azure SQL Data Warehouse Azure Analysis Services Power BI Microsoft Azure also supports other Big Data services like Azure IoT Hub, Azure Event Hubs, Azure Machine Learning, and Azure Data Lake to allow customers to tailor the above architecture to meet their unique needs. PolyBase
- © Microsoft Corporation Connected retail experience demo
- © Microsoft Corporation Connected user experience in retail Comprehensive, globally scalable infrastructure Rich Insights & campaign optimization Highly personalized & engaging experience Consumer Marketing manager Solution architect
- INGEST STORE PREP & TRAIN Connected retail ARCHITECTURE Streaming Data (unstructured) Azure Event Hubs Files (unstructured) Media (unstructured) Logs (unstructured) Azure Data Factory Azure Databricks Real-time (Web & Mobile) apps Business/custom apps (structured) Azure SQL Data Warehouse Azure Analysis Services Power BI Azure Cosmos DB Azure Blob Storage RECOMMENDATION ENGINE PRODUCT CATALOG CUSTOMER PROFILES ONLINE TRANSACTIONS SQL Recommendations
- C O N N E C T E D R E T A I L E X P E R I E N C E D E M O
- Connected user experience in retail Comprehensive, globally scalable infrastructure Rich Insights & campaign optimization Highly personalized & engaging experienceCONSUMER MARKETING MANAGER SOLUTION ARCHITECT
- Consumer
- Store Manager
- Online Marketing Manger
- Solution Architect
- INGEST STORE PREP & TRAIN Connected retail ARCHITECTURE Streaming Data (unstructured) Azure Event Hubs Files (unstructured) Media (unstructured) Logs (unstructured) Azure Data Factory Azure Databricks Real-time (Web & Mobile) apps Business/custom apps (structured) Azure SQL Data Warehouse Azure Analysis Services Power BI Azure Cosmos DB Azure Blob Storage RECOMMENDATION ENGINE PRODUCT CATALOG CUSTOMER PROFILES ONLINE TRANSACTIONS SQL Recommendations
- © Microsoft Corporation Boost operational efficiency and improve patient acre experience with intelligent detection and in time service Healthcare BIG Data and data warehouse impacts all verticals Heartland Bank prevents fraud and boosts profits The UK NHS transforms healthcare with faster access to information City of Barcelona boosts citizen unsegmented with intelligent app Rolls Royce decreases costs with Predictive Maintenance Jet.com transforms customer engagement with truly personalized experience Manufacturing Eliminate downtime and increase efficiency by enabling better predictive maintenance for your capital assets Banking Minimize losses with more accurate fraud detection and assess exposure to asset, credit, and market risk using a holistic approach Government Empower citizens and improve their engagement with relevant information and personalized citizen services Retail Turn individual customer interactions into contextual engagements and increase customer satisfaction with highly personalized offers and content
- © Microsoft Corporation Smarter incentive compensation Challenge Incentives company needed to consolidate and analyze employee behavior data at scale to create customized offerings Impact 75% reduction in storage costs and 70% cut in time spent on data collection Maritz rapidly scales up unified data model 2.5x and scales down to minimize costs DW DW DW DW ETL Unification of disparate DWs Prolonged and faulty data consolidation Elastic scale Managed environment Fast provisioning Azure SQL DWBefore Faster insights at 1 quarter the cost with SQL Data Warehouse DATA WAREHOUSING
- © Microsoft Corporation Predictive maintenance through Big Data Challenge Industrial automation business outgrew data capacity and required a full managed Big Data solution Impact Full oil and gas pipeline visibility with reduced maintenance at 90% less cost Development time cut 80% for shorter time-to-market, reduced costs, and improved customer responsiveness Fixed capacity and limited insights Complete data visibility for less Big Data storage Azure Data Factory AA & Machine Learning Maintenance costs reduced 90% with HDInsight BIG DATA HD InsightBefore
- © Microsoft Corporation Challenge Global consumer goods company struggled with poor performance on their existing business intelligence solution Impact Easily accessible data insights with Azure SQL Data Warehouse and supporting Azure services Increased cost savings on licensing and maintenance which paid for the new platform in the first year Power BI Insights on desktop & mobile Azure SQL Data Warehouse Disconnect between data storage and consumption Integrated front-end and back-end data Back-end data storage ? Front-end data consumption Azure Analysis Services & SSRS Cache Empowering 40,000 employees to work smarter using big data Hybrid business intelligence solution with Microsoft Power BI and Azure ADVANCED ANALYTICS ON BIG DATA Azure SQL Data Warehouse + Power BIBefore
- © Microsoft Corporation Challenge Diagnostics lab wanted to aggregate, analyze, and display data in a usable way, without burdening doctors with data entry Impact Integrated patient data using analytics in Azure SQL Data Warehouse to serve more customers Accurate diagnostics for behavioral health issues such as opioid addiction HIPPA compliance with physical, technical, and administrative safeguards in Azure core services Manual data input Insights from aggregated data Power BI Azure SQL Data Warehouse Visual insights Addressing the opioid crisis Aggregated health and test data with Power BI BIG DATA Azure SQL Data WarehouseBefore
- © Microsoft Corporation Challenge Italian municipality needed to manage and safeguard public information amidst increasing threats to data security Impact Improved business continuity for public record applications and reduced total cost of ownership with Azure SQL Data Warehouse Catania can now protect information, provide controlled access for municipal employees and constituents, and comply with regulation Increased security threats SQL Server Azure SQL Data Warehouse Secure hybrid data estate Delivering public services for over 1 million people Hybrid cloud solution with Azure SQL Data Warehouse and SQL Server MODERN DATA WAREHOUSING Hybrid cloud + Azure SQL Data WarehouseBefore
- © Microsoft Corporation Challenge Electronic invoicing solution was unable to scale as needed to accommodate increased invoice submissions during billing cycles Impact Processed 2.5 million invoices in the first three months after launching Reduced time to process 28,000 invoices from 2 days to 2 hours Increased transaction speed to 74 validations per second Cannot support extremes in invoices SQL Azure SQL Data Warehouse Azure SQL Database Time #ofinvoices Scalability for changes in demand Processing invoices 24 times faster Self-service portal with modern architecture created using Azure MODERN DATA WAREHOUSING Azure SQL Data Warehouse + Azure SQL DatabaseBefore
- © Microsoft Corporation Challenge Brazilian retailer began to lose money when sales stalled and they were unable to accurately discount products at the point of sale Impact Improved product pricing with real-time business intelligence Reduced costs by moving to the cloud with an advanced analytics solution including Azure SQL Data Warehouse and other Azure services Implemented 90% of the solution with no IT help Uninformed negotiation leads to loss of profits Informed sellers improve profit margins ? Improving sales negotiations to increase profits Advanced Analytics with Azure SQL Data Warehouse ADVANCED ANALYTICS Azure SQL Data WarehouseBefore
- © Microsoft Corporation Retail & consumer goods Discrete manufacturing Government & education Professional services Banking & financial services Healthcare Big Data + DW solutions in action
- © Microsoft Corporation Engage Microsoft experts for a workshop to help identify high impact scenarios Explore industry specific solutions Leverage our partner ecosystem to get started quickly on business solutions Enable your teams with Microsoft Professional Programs for Data Science & Big Data Get started now
- © Copyright Microsoft Corporation. All rights reserved.
Hinweis der Redaktion
- Let’s start by talking about how the world is changing We all aspire to create disruption that constructs new realities for customers and builds a distinct advantage for our organizations. In order to make that leap, we have to look across trends and decide: which variables and trends we need to care about which ones will prevail which ones we invest in
- We’ve identified 3 major trends we believe will heavily shape and shoulder disruption in the future The 1st is the astronomical explosion of data By 2020, data will reach 44ZB, quadruple what it is today The 2nd trend is cloud adoption 4 out of every 5 companies invest in public cloud technologies The 3rd major trend is artificial intelligence AI gives life to all that data we’re creating This year alone, investment in AI has increased 300%
- You’re probably not surprised that we’ve identified data, cloud and AI as the seeds for growing transformation. However, this isn’t a simple list of things you go out and buy The key to true business transformation – or the magic behind it – doesn’t come from any one of these on their own. It comes from the dynamic and fluid intersection You might be thinking enough of the theory, so let’s look at the facts.
- Organizations that harness data, cloud, and AI OUT PERFORM <click>and we’re not the ones saying this keystone research results that companies who invest out outperforming. Companies need to start using their data.
- Through a Keystone research study we learned companies in the top quartile for “investing in their data platform” vastly outperformed companies in the bottom quartile With double the operating margin, they are crushing the competition Investments ALONE don’t make you money and they don’t give you a competitive edge. Investments enable innovation. This leads us to our second point that….
- What prevents companies from capitalizing on that data growth? Data exists in silos: There are all sorts of data coming in, stored across databases that power the applications or lifeblood of a company, data from sensors and devices, external data sources from media, and more. But harnessing that data is difficult and costly. Furthermore, connecting that data in ways that drives deeper insights poses even greater challenges. Incongruent data types: Different applications , different data formats. There is tremendous complexity in bringing these formats together in a way where companies can leverage varietal data sources to derive insights that are richer than the single source. Performance constraints: On-premises systems reach capacity. Customers are having to make strategic decisions about how to scale and how to do it cost effectively. Complexity of solutions: Over time, companies have adopted a variety of products or solutions to solve specific business needs. They now find that they have a diverse set of tools, on premise and in the cloud, all with their unique tool sets and cost models, and skill sets requires to use them. It is hard to skill up and scale these offerings for the whole company to take advantage of, and costing is no longer transparent…the invoice has many line items to manage. This comes to rising costs. Scaling up and adding hardware, paying for multiple toolsets that have some overlapping capabilities but lacking the scale, or ubiquity to do more than a purpose driven task well, and hiring resources with the specific skills needed adds up. And suddenly, companies looking to get ahead and maximize ROI with data are finding it challenging to harness it for transformation.
- To derive real value from your data, msft provides a comprehensive platform with familiar tools and a robust ecosystem of partners and ISVS to deliver the solutions you need. One hub for all data: This is one of the most significant pain points we hear from customers. How can I get access to all of the data internally and externally to my organization. Each system needs its own database, that is true. Microsoft’s analytics solution is able to connect to those data sources and bring that data into a cloud scale data warehouse solution for deriving insights, from a variety of data sources. Diverse data types: This data can be in various formats, structured, and unstructured. It doesn’t matter. Microsoft’s solution can process the data into formats that can be leveraged in new and exciting ways. Unlimited data scale: Not only can Microsoft’s solution process these diverse types of data, it can do it at scale, without constraints typically associated with existing on premises solutions. Familiar tools and ecosystem: And, you don’t have to hire a number of specialists to manage a niche tool. Microsoft’s offerings provide familiar tools and ecosystem to help you leverage your investment quickly. Lower TCO: The benefits noted all accrue to a lower TCO.
- Only Microsoft delivers a comprehensive cloud scale analytics solution. First, you need to be able to ingest data where ever that data is, whatever type of structured, unstructured, streaming data. Once you’ve ingested this data you need a cloud scale storage solution that can handle all data types at scale and cost effectively. Once stored data, you want to be able to prepare and process your data, to transform unstructured data and serve it with your structured data into the data warehouse. Finally, you can leverage that data in a number of ways. Deliver it to the organization with rich visualizations for improved BI and reporting, deliver more advanced analytics such as personalized customer experiences, and access real-time data all at your fingertips for better business outcomes (for example, predictive maintenance). In this example, we’ve integrated data into a single hub to provide intelligent insights with rich BI, Advanced Analytics, and Real Time analytics to drive the business forward.
- Productive: quickly build solutions and focus on biz value: all the tools and services you need on a single platform, reducing complexity. (familiar tools) Familiar tools to quickly go to market: example Provision your data warehouse & spark environment in minutes with a single click Accelerate your data integration with consistent 70+ native data connectors Empower your data scientists, data engineers, and business analysts to use the tools and languages of their choice Hybrid: meet you where you are: cloud is a journey – customers environments on premise and cloud, evolve with your needs. How we are Hybrid: Leverage SQL Server’s proven performance and security consistently, in public or private cloud Reduce cost & complexity of managing your existing data transformations by consistently running SQL Server Integration Service packages in Azure Enable consistent user experience with common identity across on-premises and Azure Intelligent: Tools that drive intelligence not only in the cloud but at the edge. You can move your analytics and machine learning closer to your data. Need more detail Our customers understand the value the of intelligent cloud and are using it to connect their businesses and customers globally. For certain scenarios, they are also recognizing the need to process and analyze data close to where the data originates. This is the power of intelligent edge. Customers are increasingly packaging applications in containers and deploying them onto their devices themselves, closer to where the data is created. This enables organizations to build machine learning and AI solutions that not only scale out with their needs, but also provide immediate inferences from intelligence built on the edge. Trusted: Local availability enables you to be compliant where your data resides. This allows mission-critical work of crucial organizations to take advantage of local availability. This addresses the requirements of governments and national infrastructure, including banks, utilities, transport and telecommunications. We want to ensure that we build our cloud infrastructure to serve the needs of customers by driving innovation and making it accessible globally. 50+ industry and geographical compliances Azure Active Directory: role based admin control on your data access. SQL Server & GDPR Compliance
- Rich partner network to accelerate time to value.
- A modern data warehouse lets you bring together all your data at any scale easily, and to get insights through analytical dashboards, operational reports, or advanced analytics for all your users. 1 Combine all your structured, unstructured and semi-structured data (logs, files, and media) using Azure Data Factory to Azure Blob Storage. 2 Leverage data in Azure Blob Storage to perform scalable analytics with Azure Databricks and achieve cleansed and transformed data. 3 Cleansed and transformed data can be moved to Azure SQL Data Warehouse to combine with existing structured data, creating one hub for all your data. Leverage native connectors between Azure Databricks and Azure SQL Data Warehouse to access and move data at scale. 4 Build operational reports and analytical dashboards on top of Azure Data Warehouse to derive insights from the data, and use Azure Analysis Services to serve thousands of end users. 5 Run ad hoc queries directly on data within Azure Databricks.
- Transform your data into actionable insights using the best-in-class machine learning tools. This architecture allows you to combine any data at any scale, and to build and deploy custom machine learning models at scale. 1 Bring together all your structured, unstructured and semi-structured data (logs, files, and media) using Azure Data Factory to Azure Blob Storage. 2 Use Azure Databricks to clean and transform the structureless datasets and combine them with structured data from operational databases or data warehouses. 3 Use scalable machine learning/deep learning techniques, to derive deeper insights from this data using Python, R or Scala, with inbuilt notebook experiences in Azure Databricks. 4 Leverage native connectors between Azure Databricks and Azure SQL Data Warehouse to access and move data at scale. 5 Power users take advantage of the inbuilt capabilities of Azure Databricks to perform root cause determination and raw data analysis. 6Run ad hoc queries directly on data within Azure Databricks. 7 Take the insights from Azure Databricks to Cosmos DB to make them accessible through web and mobile apps.
- Get insights from live streaming data with ease. Capture data continuously from any IoT device, or logs from website clickstreams, and process it in near-real time. 1 Easily ingest live streaming data for an application using Apache Kafka cluster in Azure HDInsight. 2 Bring together all your structured data using Azure Data Factory to Azure Blob Storage. 3 Take advantage of Azure Databricks to clean, transform, and analyze the streaming data, and combine it with structured data from operational databases or data warehouses. 4Use scalable machine learning/deep learning techniques, to derive deeper insights from this data using Python, R or Scala, with inbuilt notebook experiences in Azure Databricks. 5 Leverage native connectors between Azure Databricks and Azure SQL Data Warehouse to access and move data at scale. 6 Build analytical dashboards and embedded reports on top of Azure Data Warehouse to share insights within your organization and use Azure Analysis Services to serve this data to thousands of users. 7 Power users take advantage of the inbuilt capabilities of Azure Databricks and Azure HDInsight to perform root cause determination and raw data analysis. 8 Take the insights from Azure Databricks to Cosmos DB to make them accessible through real time apps.
- Unstructured data / real time streaming from web logs, weather, twitter trends, events. Structured data (historical) from sales, marketing , company. 1 Easily ingest live streaming data for an application using Apache Kafka cluster in Azure HDInsight. 2 Bring together all your structured data using Azure Data Factory to Azure Blob Storage. 3 Take advantage of Azure Databricks to clean, transform, and analyze the streaming data, and combine it with structured data from operational databases or data warehouses. 4Use scalable machine learning/deep learning techniques, to derive deeper insights from this data using Python, R or Scala, with inbuilt notebook experiences in Azure Databricks. 5 Leverage native connectors between Azure Databricks and Azure SQL Data Warehouse to access and move data at scale. 6 Build analytical dashboards and embedded reports on top of Azure Data Warehouse to share insights within your organization and use Azure Analysis Services to serve this data to thousands of users. 7 Power users take advantage of the inbuilt capabilities of Azure Databricks and Azure HDInsight to perform root cause determination and raw data analysis. 8 Take the insights from Azure Databricks to Cosmos DB to make them accessible through real time apps.
- Demo scenario: Showcase how this all comes together in a real world scenario through a mobile application. We’ll talk about how the consumer is offered a highly personalized and engaging experience. This experience was made possible through the rich insights the marketing manager was able to act upon within his/her campaign. And finally, how all of this is made possible on Azure with the underlying solution delivered by the solution architect.
- As a consumer: I open my application and I’m trying to buy shoes. Currently I see a generic experience (all shoes).
- I log into the application…
- I sign in as As Sunil
- As soon as I sign in the application ensures I have security enabled through my sign in experience via pin code or fingerprint. This ensures your sign in experience as a consumer is easier. (What is the feature?)
- Now this experience is more personalized to me…it has learned from my history that I prefer brighh colors like orange and green…so now I am more engaged with this application. I go ahead and click on the green shoe.
- I love this shoe, I’ve probably visited this page a number of times. But the price tag is a bit high for me…and I’m still not over the edge to want to purchase this right away.
- So I scroll down and look for more reviews on how people have reacted to this shoe and after seeing a 4 star rating, I’m a little bit more convinced.
- But I still don’t know the price tag is a bit out of my budget. But while I’m on this page…
- A promotion pops up that is so relevant to me and what I have been browsing…I’ve been provided a discount that if I purchase this right now I’ll get a 20% off…and not only that, if I decide to pick it up at the store I get an additional 15% discount. This is an offer I cannot refuse. So I select the option to pick it up at the store.
- I add this to the cart after selecting the discount and now it’s only $64. I will now check out.
- Since we signed up for improved security, the app is able to make my experience so much easier by not having to reenter my details every time I check out. Instead I just use my fingerprint to auto update and I’m ready to check out.
- When I check out it says my fingerprint is a match so I make the purchase
- I’m provided a scan code that I need to take to the store to check out with.
- When I go to the store, the store manager, is able to upsell to me because of the data they already have about my purchasing behavior and likes. For instance, when I’m at the store to pick up the shoes, the store manager can show me some shoelaces or tennis shorts I might be interested in to buy along with my new shoes.
- Now le’s go through the experience of the online marketing manager for this shoe store. As a marketing manager I want to have as much data as possible about how customers are engaging with my product and the applications so that I am able to run the right campaigns and engage with my customers intelligently. Here’s a dashboard…
- Here’s a dashboard that allows me to view my business end to end.
- It provides me with sentiment by product category so I can better understand how my consumers are reacting to the different lines of business that I own, like mens shoes, sports accessories, women’s clothing, etc…all in one place.
- Now I am able to see what consumers are clicking on so I can understand my most popular categories.
- Not only that, I can see how consumers feel about my product portfolio by looking at what they are tweeting about and what is popular across the globe. I can use this data for product development as well as how I run my customized campaigns.
- I can also right from my seat see the revenue that I have across the globe to understand where I need to run my campaigns. But I want to be able to see how my product categories are performing in terms of average revenue...
- So I click on this dashboard
- These are the different categories of my products and I see that this grey dot is performing well. Let’s see what it is.
- This is mens casual shoes. Looks like the campaign I just ran for sunil and other customers is working well. But I see the red dot is not performing as well. Let’s look at what that category is.
- Mens clothing. I’m wondering if I need to run a campaign. But I don’t think I have enough information to be able to run a campaign right away. So now I want to go into my campaigns dashboard.
- This is a very handy tool that I have where from one single place I am able to see and analyze my campaign data and results from across the globe.
- I can see my revenue cost and return on investment of the various campaigns I have run on the product categories.
- I can also see up top my aggregated data of all of my global campaigns and how they are performing against my target.
- I can also see the various types of channels that I am running campaigns through and the ROI on those channels. For instance, through emails, through social media, through events, etc…
- But I am most interested in the intelligence built into this recommended campaign dashboard. Let’s zoom into this dashboard
- Based on the data of the campaigns that I am running globally, combined with the data about how my consumers are engaging with the different product categories, how the popularity of these categories has been evolving, I have a recommended campaign dashboard all based on the rich data my solution is built upon. Here I see the ROI that I will expect if I run certain campaigns. My solution intelligently recommends that if I ran a campaign on mens clothing I will get a ROI of 82%. Not only that, it tells my what type of campaign I should be running to maximize this ROI, which is a 10% discount campaign. I think I have enough information to run this campaign. While I am sitting here I launch this campaign.
- It ask me to confirm the campaign launch
- And I click on OK.
- Now le’s go how all of this magic was created in the back end.
- Unstructured data / real time streaming from web logs, weather, twitter trends, events. Structured data (historical) from sales, marketing , company. 1 Easily ingest live streaming data for an application using Apache Kafka cluster in Azure HDInsight. 2 Bring together all your structured data using Azure Data Factory to Azure Blob Storage. 3 Take advantage of Azure Databricks to clean, transform, and analyze the streaming data, and combine it with structured data from operational databases or data warehouses. 4Use scalable machine learning/deep learning techniques, to derive deeper insights from this data using Python, R or Scala, with inbuilt notebook experiences in Azure Databricks. 5 Leverage native connectors between Azure Databricks and Azure SQL Data Warehouse to access and move data at scale. 6 Build analytical dashboards and embedded reports on top of Azure Data Warehouse to share insights within your organization and use Azure Analysis Services to serve this data to thousands of users. 7 Power users take advantage of the inbuilt capabilities of Azure Databricks and Azure HDInsight to perform root cause determination and raw data analysis. 8 Take the insights from Azure Databricks to Cosmos DB to make them accessible through real time apps.
- This is just one example of how Business Analytics and AI can help transform the business process for an organization. We have customers across every industry with similar stories for transformation: - For example, Rockwell, a global industrial automation company, who cut develop time by 80% and costs up to 90% by providing full time pipeline visibility, - or, QuarterSpot, a start up in the financial sector, who lowered loan defaults by 50%, leading to increased profitability
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