Matt Davis — A Member of The Gartner Blog Network. Matthew Davis Research Director 3 years at Gartner 12 years IT Industry Matt Davis is a Supply Chain Research Director and lead analyst for Supply Chain Strategy. Read Full Bio On-Demand Supply Chain Webinars by Matt Davis | August 21, 2013 | 1 Comment ”Would anyone actually sit for 45 minutes or so to listen to a webinar?” A common link to communication issues in both broader supply chain strategy and in segmentation initiatives is that people use the same term to mean different things AND they have different ideas on the scope of the work. Below, I have included five of the highest rated webinars we’ve recently recorded. How to Create a Demand-Driven Supply Chain Strategy An understanding of the journey to demand-driven maturity will aid in communicating a long-term supply chain vision and sequencing improvement activities to achieve the vision faster.
Massive & Messy: The New Goldmine of BIG DATA. The ability to collect, integrate, and meaningfully analyze huge volumes of disparate data – all within the acceptable elapsed time – is becoming a key competitive differentiator across all sectors.
Big Data Analytics is helping companies gather superior and real-time market intelligence, reduce product development time, eliminate defects, attract more customers, enhance customer service etc., and do a lot more. But then, we are not talking of regular spreadsheets here… Simply put – “Big Data” means too large and diverse data sets for traditional enterprise tools to process within tolerable elapsed time. From the dawn of civilization until 2003, humankind generated 5 exabytes of data. Now we produce 5 exabytes every two days. Data globally is expected to grow 44 times, i.e., from 0.8 zettabyte in 2009 to 35.2 zettabyte in 2020. In the last couple of years, the world has witnessed an explosion in the volumes of data, while also creating almost 90% of the data that exists today. Big data use cases – What is a big data use case and how to get started. The 5 game changing big data use cases While much of the big data activity in the market up to now has been experimenting and learning about big data technologies, IBM has been focused on also helping organizations understand what problems big data can address.
We’ve identified the top 5 high value use cases that can be your first step into big data: Big Data ExplorationFind, visualize, understand all big data to improve decision making. Big data exploration addresses the challenge that every large organization faces: information is stored in many different systems and silos and people need access to that data to do their day-to-day work and make important decisions.Enhanced 360º View of the CustomerExtend existing customer views by incorporating additional internal and external information sources. Operations Analysis Use Case Get the solution sheet What is a use case? A use case helps you solve a specific business challenge by using patterns or examples of technology solutions. 21 Key Performance Indicators for Ecommerce Businesses. Key performance indicators are becoming common in large corporations as a way to measure and monitor the success of key activities.
But they can also play an important role in any sized ecommerce business. A KPI — key performance indicator — is simply a measure of some process, event, or activity. An example is checkout abandonment, when shoppers exit before completing an order. This KPI should be monitored closely by all ecommerce businesses. If it is typically 10 percent and suddenly goes to 15 percent, that may be an indicator that something is broken on your website, like your SSL, your shipping estimator, or your credit card authorization. Establishing KPIs KPIs differ among businesses. For strategic and operational planning, KPIs are also used for SMART goals — Specific, Measurable, Achievable, Realistic, Time-bound. All businesses should regularly monitor their revenue, cash position, receivables, payables, and basic accounting reports. 21 KPIs for Ecommerce What’s a Normal KPI?
5 Big Data Use Cases. Big Data Analytics - Airlines Use Case. New Tools for New Times – Primer on Big Data, Hadoop and “In-memory” Data Clouds. Data growth curve: Terabytes -> Petabytes -> Exabytes -> Zettabytes -> Yottabytes -> Brontobytes -> Geopbytes.
It is getting more interesting. Analytical Infrastructure curve: Databases -> Datamarts -> Operational Data Stores (ODS) -> Enterprise Data Warehouses -> Data Appliances -> In-Memory Appliances -> NoSQL Databases -> Hadoop Clusters In most enterprises, whether it’s a public or private enterprise, there is typically a mountain of data, structured and unstructured data, that contains potential insights about how to serve their customers better, how to engage with customers better and make the processes run more efficiently.
Consider this: Big Data and Advanced Analytics. Big Data Analytics Use Cases. Are you data-flooded, data-driven, data informed? Are you insight driven or hindsight driven? Are you a firm where executives claim – “Data is our competitive advantage.” Or sprout analogies like, “data is the new oil”. The challenge I found in companies is not about having a 100,000 ft view of the importance or value of data. The challenge is the next step….so, how are you going to create new data products? 2011 May. Data growth curve: Terabytes -> Petabytes -> Exabytes -> Zettabytes -> Yottabytes -> Brontobytes -> Geopbytes.
It is getting more interesting. Analytical Infrastructure curve: Databases -> Datamarts -> Operational Data Stores (ODS) -> Enterprise Data Warehouses -> Data Appliances -> In-Memory Appliances -> NoSQL Databases -> Hadoop Clusters In most enterprises, whether it’s a public or private enterprise, there is typically a mountain of data, structured and unstructured data, that contains potential insights about how to serve their customers better, how to engage with customers better and make the processes run more efficiently. Consider this: Data is seen as a resource that can be extracted and refined and turned into something powerful. What business problems are being targeted? Why are some companies in retail, insurance, financial services and healthcare racing to position themselves in Big Data, in-memory data clouds while others don’t seem to care? Read more Like this: Like Loading... The Vendor Landscape of BI and Analytics. By Ravi Kalakota “In God we trust, all others bring data” The “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions” is a differentiating causal chain in business today.
To service this “data->decision” chain a very large industry is emerging. The Business Intelligence, Performance Management and Data Analytics is a large confusing software category with multiple sub-categories — mega-vendors (full stack, niche vendors, data discovery, visualization, data appliances, Open Source, Cloud – SaaS, Data Integration, Data Quality, Mobile BI, Services and Custom Analytics). But the interest in BI and analytics is surging. Here is a list of vendors who participate in this marketspace: Big Data Startup and Existing Companies to Watch The BI and Analytics Stack is getting incredibly complex. Given the complexity and the non-linear innovation taking place we expect the market fragmentation to continue for a while before we see a wave of consolidation. Like this: Like Loading... Massive & Messy: The New Goldmine of BIG DATA. Top 10 Big Data Stories Of 2013.
Big data equals big opportunity -- and a surplus of hype. Catch up on the big data articles that interested readers most in 2013. Big Data Talent War: 7 Ways To Win (click image for larger view and for slideshow) Big data ruled as one of the most popular tech topics of 2013, drawing reader interest along many different angles of coverage. Whether focused on careers and education, emerging platforms and technologies, or real-world use cases from healthcare to celebrity social networking, our big data coverage during the last year drew millions of page views. For a look back at what you may have missed, here's our list of the top-ten big data headlines of 2013. 1. 2. 7 Big Data Solutions Try To Reshape Healthcare. Big Data Use-Case: ETL made easy – Jim Kaskade. This Big Data use-case involves a Global Fortune 100.
The company is interested in rethinking how they manage the many disparate billing systems and data marts which IT supports within its multiple divisions. The data from the systems is provided in multiple formats including: flat files, feeds, and SQL extracts. Question: So what’s the issue? Why not just use IBM’s Datastage and SQL in Teradata? Answer: Maybe because it’s expensive? This is where Hadoop can provide a very cost-effective ETL platform which manages all aspects of data integration while still addressing requirements in scalability and usability. Think about it.