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Published 9/17/15 at 1:24 PM by Mark Barrenechea
637 view(s), 6 edit(s) since 9/17/15

Digital engagement isn’t an option anymore, it’s a requirement. Today’s consumers are savvy and fickle, and companies must work to earn their loyalty. They’re demanding more from the brands they love, and their tolerance for anything but a seamless, engaging, and compelling experience is flagging.

In a digital world, organizations must digitize their customer journeys, from initial interest through to purchase and follow-on service or support. The best way to do this is to shift to a digital marketing strategy. One that creates consistent and compelling customer experiences at every touchpoint through omni-channel delivery, responsive design, and targeted communications and information.

Digital technologies have introduced new customer touchpoints and increased opportunities to engage. Since consumers often use more than one channel to interact with a brand (in some instances they use five or six), delivering uniform and relevant messages across all channels is crucial for return on marketing investments and customer satisfaction. Omni-channel focuses on meeting consumer needs by pulling together programs to provide a cohesive brand experience across channels, platforms, and devices.

Fluidity: The Principle of Responsive Web Design

To borrow from Bruce Lee, digital design should “be like water”. You put water into a cup, it becomes the cup. You put water into a bottle, it becomes the bottle. You put water into a teapot, it becomes the teapot. The same holds true for digital experiences. The transition from desktop to device to point-of-sale should be fluid. This is achieved through responsive design. Customers don’t see individual devices or channels; they look for a consistent and familiar brand experience that delivers relevant content.

Nirvana on the customer journey is realized when a company anticipates the needs and wants of a customer and serves up targeted and tailored content, products, or services, in the moment of need, wherever the customer is. Organizations that can predict customer behavior have a better chance at fulfilling consumer needs.

Analytics—or analyzing data collected across various touchpoints of the customer journey (transactions, interactions, social media sites, and devices) helps organizations discover valuable customer insights so that they can offer more personalized and satisfying experiences.

The most effective way to target different audiences is to use messages that focus on products and services with the greatest appeal for each segment. Using dynamically generated customer communications, organizations can create and automate their marketing campaigns. When correspondence is part of a digitized process, end results are gains in efficiency and the ability to create superior customer experiences.

As one of the foundational suites for Enterprise Information Management (EIM), Customer Experience Management (CEM) aims to create a richer, more interactive online experience across multiple channels without sacrificing requirements for compliance and information governance. CEM brings together all of the technologies required to re-architect back-office systems, consolidate customer data, and create digitized front-end experiences.

Digital engagement starts inside the firewall and extends outside the enterprise and all along the supply chain. In the next post in this series, I’ll explore how the supply chain is being disrupted and how enterprises can digitize key processes for greater collaboration, information exchange, and business agility. Find out how you cancapitalize on digital disruption.

To learn more, read my book, Digital: Disrupt or Die.


The Top Tech Trends To Watch: 2016 To 2018 — Should Help Accelerate CIOs’ BT Agenda

CIOs already face significant pressure to understand and respond to digitally empowered customers. And as their firms’ customer experience (CX) focus intensifies, CIOs must bring digital into the heart of customer engagements — leveraging technology to assure high value end to end across the customer life cycle.

The next wave of tech trends to watch — 2016 to 2018 — support tech management’s move to the heart of digital CX implementation. Today’s mainstream CX investment path has individual organizations making point investments in the latest technology inventions — like social, mobile, big data, cloud, and analytics. But today’s leading firms are delivering solutions that reach end to end across customers’ journeys and across systems that connect the employees who service the customer life cycle. And these trends will accelerate over the next three years.

We see the top tech trends making this shift in three phases from 2016 through 2018:

■        Visionaries will dominate dawning phase trends as they drive point inventions to address specific business organizations’ opportunities.

■        Fast followers will discover the limits of point solutions in the awareness phase and begin to work through the challenges of end-to-end innovation.

■        Enterprises will shift investment toward integrating capabilities across the customer life cycle in the acceptance phase. We call this an “insight-out” approach, as it reaches across customers’ ecosystems.

In our research report, we plot this year’s top technology trends to watch across these three phases. The figure below summarizes our trends. It also illustrates where we think each is relative to point and end-to-end investments. And the figure names some research reports to check out for more information.

CIOs should work with the other business leaders and their tech management teams to adopt the capabilities necessary to leverage these trends. This work begins with the enterprise architects planning appropriate shifts in technology use for their firms. To enable effective adoption, CIOs should invest in:

■        Customer-in strategy and design.

■        Fast-cycle planning and governance.

■        Continuous delivery.

■        Flexible sourcing.

■        Information security.

Take advantage of our upcoming Webinar to get a clearer understanding of the top tech trends to watch — 2016 to 2018 — and how your firm can take advantage of them.

13 more big data & analytics companies to watch


Founded: 2012
Headquarters: Redwood City, Calif.
Funding/investors: $9M in Series A funding led by Costanoa Capital and Data Collective.

Focus: Its data accessibility platform is designed to make information more usable by the masses across enterprises. The company is led by former Oracle, Apple, Google and Microsoft engineers and executives, and its on-premises and virtual private cloud-based offerings promise to help data analysts get in sync, optimize data across Hadoop and other stores, and ensure data governance. Boasts customers including eBay and Square.

Founded: 2012
Headquarters: Menlo Park, Calif. (with operations in India, too)
Funding/investors: $15M in Series B funding led by Scale Venture Partners and Next World Capital, bringing total funding to $23M.

Focus: Data science-driven predictive analytics software for sales teams, including the newly released Aviso Insights for Salesforce. Co-founder and CEO K.V. Rao previously founded subscription commerce firm Zuora and worked for WebEx, while Co-founder and CTO Andrew Abrahams was head of quantitative research and model oversight at JPMorgan Chase. The two met about 20 years ago at the National Center for Supercomputing Applications.

Founded: 2004
Headquarters: San Francisco
Funding/investors: $156M, including a $65M round in March led by Wellington Management.

Focus: Cloud-based business intelligence and analytics that works across compliance-sensitive enterprises but also gives end users self-service data access. This company, formed by a couple of ex-Siebel Analytics team leaders, has now been around for a while, has thousands of customers and has established itself as a competitor to big companies like IBM and Oracle. And it has also partnered with big companies, such as AWS and SAP, whose HANA in-memory database can now run Birst’s software.
Founded: 2012
Headquarters: Mountain View
Funding/investors: $39M, including a $20M Series C round led by Intel Capital in August.

Focus: A founding team from VMware has delivered the EPIC software platform designed to enable customers to spin up virtual on-premises Hadoop or Spark clusters that give data scientists easier access to big data and applications

Founded: 2009
Headquarters: San Francisco
Funding/investors: $76M, including $40M in Series E funding led by ST Telemedia.

Focus: Big data analytics application for Hadoop designed to let any employee analyze and visualize structured and unstructured data. Counts British Telecom and Citibank among its customers.

Deep Information Sciences
Founded: 2010
Headquarters: Boston
Funding/investors: $18M, including an $8M Series a round in April led by Sigma Prime Ventures and Stage 1 Ventures.

Focus: The company’s database storage engine employs machine learning and predictive algorithms to enable MySQL databases to handle big data processing needs at enterprise scale. Founded by CTO Thomas Hazel, a database and distributed systems industry veteran.
Founded: 2012
Headquarters: Santa Cruz
Funding/investors: $48M, including a $30M B round in March led by Meritech

Focus: Web-based business intelligence platform that provides access to data whether in a database or the cloud. A modeling language called LookML enables analysts to create interfaces end users can employ for dashboard or to drill down and really analyze data. Founded

Founded: 2012
Headquarters: Palo Alto
Funding: $14M, including $11M in Series A funding in May, with backers including Chevron Technology Ventures and Intel Capital.

Focus: Semantic search engine that plows through big data from multiple sources and delivers information in a way that can be consumed by line-of-business application users. The company announced in June that its platform is now powered by Apache Spark. Co-founder Donald Thompson spent 15 years prior to launching Maana in top engineering and architect jobs at Microsoft, including on the Bing search project.
Founded: 2007
Headquarters: Cambridge, Mass.
Funding/investors: $20M, including $15M in Series B funding led by Ascent Venture Partners.

Focus: This company, which got its start in Germany under founder Ingo Mierswa, offers an open source-based predictive analytics platform for business analysts and data scientists. The platform, available on-premises or in the cloud, has been upgraded of late with new security and workflow capabilities. Peter Lee, a former EVP at Tibco, took over as CEO in June.

Founded: 2011
Headquarters: Redwood Shores, Calif.
Funding/investors: $10M in Series A funding in March, from Crosslink Capital and .406 Ventures.

Focus: The team behind Informatica/Siperian MDM started Reltio, which offers what it calls data-driven applications for sales, marketing, compliance and other users, as well as a cloud-based master data management platform. The company claims its offerings break down silos between applications like CRM and ERP to give business users direct access to and control over data.

Founded: 2014

Headquarters: Palo Alto

Funding/investors: $900K in seed funding from investors including Andreessen Horowitz and Formation8.

Focus: A “data science platform for the unstructured world.” Sensai’s offering makes it possible to quantify and analyze textual information, such as from news articles and regulatory filings. The company is focused initially on big financial firms, like UBS, though also has tech giant Siemens among its earlier customers. Two of Sensai’s co-founders come from crowdfunding company Rally.org.

Founded: 2014
Headquarters: Seattle

Funding/investors: $13.25M, including a $10M Series A round led by Foundry Group, New Enterprise Associates and Madrona Venture Group

Focus: This iPhone app enables businesses to tap into smartphone users (or “Fives”) to clean up big data in their spare time for a little spare cash. The idea is that computing power alone can’t be counted on to crunch and analyze big data. Micro-tasks include everything from SEO-focused photo tagging to conducting surveys.

Treasure Data
Founded: 2011
Headquarters: Mountain View
Funding/investors: $23M, including $15M in January in Series B funding led by Scale Venture Partners.

Focus: Provides cloud services designed to simplify the collection, storage and analysis of data, whether from mobile apps, Internet of Things devices, cloud applications or other sources of information. This alternative to Hadoop platforms and services handles some 22 trillion events per year, according to the company, which has a presence not just in Silicon Valley, but in Japan and South Korea as well.

Birst announced new “Networked BI” capabilities

Today Birst announced our new “Networked BI” capabilities, which completely redefine the way BI is delivered and consumed. “Networked BI” enables global governance with local execution and further realizes Birst’s vision of trusted and agile collaboration between centralized and decentralized teams.

Organizations have traditionally depended on centralized IT to physically replicate data and metadata infrastructures to enable analytics for decentralized groups, but this approach is time-consuming, expensive and, ultimately, a barrier to end-user self-service. Alternatively, “Networked BI” virtualizes the entire BI ecosystem, transforming every aspect of an organization’s approach to analytics, from application development lifecycles to end-user generated data mashups and content.

Built on top of Birst’s modern, multi-tenant cloud architecture, “Networked BI”:

  • creates a network of interwoven BI instances that share a common analytical fabric.
  • enables organizations to expand the use of BI across multiple regions, departments and customers in a more agile way.
  • empowers decentralized groups to augment the global analytical fabric with their own local data.
  • delivers enterprise-grade scalability at unprecedented speed.
  • provides end-user freedom with self-service data preparation capabilities and transparent governance.
  • eliminates data silos once and for all and dramatically accelerates the delivery of BI across the enterprise.

Learn more by registering for a webinar hosted by Birst, featuring Martha Bennett from Forrester Research. You can also read our news release and blog post.

Data transformation process at IHS for Info Quality

Source: IHS Global

We have standardized the data transformation process into seven steps. The order of the steps and the need to have quality checks throughout the process is important because the quality of each step is dependent on the quality of all of the preceding steps. The seven-step process we follow in transforming data into critical information and insight involves the following:

Sourcing: We locate hundreds of possible data sources and then evaluate them for correctness, currency and completeness.

Capture: We collect documents and digital feeds, harvest content from publicly available sources, visit sites for updates, etc. Once the data is aggregated, we validate and normalize the data before loading it into our proprietary databases.

Matching:We link disparate instances of the same attribute. This knowledge-based activity ensures consistency over time and across sources, eliminating unlinked information about a single well, a single part, a single chemical, etc.

Identification: We attach an IHS identifier to matched information to ensure that the matched information stays linked. We also confirm that industry standard identifiers, which often vary over time, are accurate and appropriately matched to the IHS identifier.

Relationships :We identify logical relationships and associations between entities and link those relationships through identification numbers. Examples include corporate parent and subsidiary relationships, leases and associated wells, international standards, and national standards. This step supplies the context for analysis.

Analysis : We use our industry experts to review, analyze, and add context and editorial commentary to the data in order to transform it into critical information and expert analysis for our customers.

Modeling and Forecasting: We utilize our critical information and expert analysis to produce additional insight by providing unbiased research and intelligence with proprietary models and forecasting tools. Our experts use their extensive experience to build models and forecasting tools for our customers’ use.   Using this proven seven-step process and the “4 Cs” of quality, we transform data into critical information and insight that is both useful to our customers and available where and when they need it. This process also provides the foundation for IHS to create integrated solutions that combine our product and services to create unique solutions for our customers in our target industry sectors.

4C’s of Information Quality

Just as 4 C’s of Diamond that determine price. For Information Quality, there are  4C’s.  What are they? IHS, world’s prominent, leading intelligence group explains.
We convert raw data into information through a series of transformational steps that reduce the uncertainty that is inherent in unrefined data. At each step along the way, we work to ensure quality of the data transformation across four dimensions, which we call the “4 Cs”:
Correctness: Validate data accuracy through comparison to external reference points
Currency: Deliver new and updated content in a timely manner
Completeness: Provide the right data attributes and analysis to ensure customers have all of the necessary information to make critical decisions
Consistency :Standardize identifiers and content across databases and products to be sure customers receive consistent information regardless of product platform