Understanding TCO: How to Avoid the Four Common Pitfalls that May Lead to Skyrocketing Bills After Implementing a BI Solution

In this special guest feature, Ulrik Pedersen, Chief Operations Officer at TARGIT,  highlights the constant battle between IT and finance on Total Cost of Ownership (TCO) when it comes to implementing a new BI solution. But, with IT budgets increasingly moving hands from IT departments to specific lines of business that may not be aware of this concept, TCO can quickly become a convoluted quagmire. Ulrik Pedersen joined the TARGIT team as Project Manager in 1999. Since then, he’s taken on the challenge of penetrating the North American market with TARGIT’s Business Intelligence and Analytics solution. Ulrik holds a Master of Science in Economics and Business Administration, B2B Marketing from Aalborg University.

Just about any savvy IT or business professional today understands the value a business intelligence (BI) solution can bring to an organization. From uncovering new sales opportunities to measuring growth to streamlining processes, BI solutions provide many benefits to the organization. However, those benefits come with a price tag. Often the total cost of a BI solution isn’t necessarily in accordance with the value it brings.

Organizations need to think carefully before investing in a BI solution to ensure they are aware of hidden costs. Total Cost of Ownership (TCO) isn’t as simple as just adding up infrastructure plus people. In reality, software only accounts for a fraction of the total cost of a BI project, and there are many other direct and indirect costs that rise steadily up front and over time. Having a full understanding of the time and resources a BI solution will cost your organization beyond the initial price tag is essential. These are the four most common pitfalls IT and business leaders should avoid to drive the most value from a BI solution.

1 – Poor Data Quality

The first step in implementing a BI project is pulling data into the data warehouse from the various other corporate systems such as the CRM, HR, and finance systems. Unfortunately, this is also one of the most time-consuming and costly steps because the data must first be cleansed and brought up to standard.

Cleansing and updating data is a long, arduous process that typically comes with a high price tag by the consultants that have to do it. It doesn’t take long for those consultancy hours to add up in a significantly expensive way.

2 – The Never Ending Project

Otherwise known as “scope creep,” long-stretch projects plague companies who struggle to select the most important data to bring into a BI project. Unfortunately for many of these companies, it’s impossible to truly know which data sets they want until they see the numbers. By then, a consultant or data scientist has already taken the time—and handed over the bill—for incorporating that data.

This results in a seemingly never ending process of starting and stopping the BI project. Worse, it’s not uncommon to see corporate priorities change before any analytics objectives can be obtained, rendering everything already done up until that point useless. The business world is changing so rapidly that a slow BI implementation can mean no BI at all.

3- License Creep

License creep refers to the uncontrolled growth in software licenses within a company. The ultimate goal of any successful BI implementation is to spread the power of analytics to as many users as possible throughout the company. But with many BI solutions, each additional user comes with a price tag, regardless of their level of BI involvement.

Additionally, rolling out an enterprise-wide BI solution usually necessitates additional servers.

It isn’t fair to say license creep is the result of poor project management. Rather, it is a result of unrealistic planning of license cost related to a successfully adopted BI solution. Imagine TCO as a line chart: license creep is where that line takes a dramatic 45-degree projection up from the initial cost. Over time, that final price tag can be double the estimated price was originally quoted.

4- The Under-Utlization Obstacle

A powerful BI and analytics solution is worthless if users aren’t armed with the know-how they need to take advantage of the various levels of tools. Companies are often won over with the words “self-service” only to discover that quite a bit of technical expertise is needed and when business decision makers need to dig in to further details, they need expensive consultants to help.

As a result, an overall under-utilization of the BI platform ensures the ambition of transformation into a data-driven company will never be realized, nor will ROI. Opportunities are lost on multiple scales, including the very basic objective of eliminating different data-truths that are floating around a company and aligning every decision-maker with the true data they need.

The Bottom Line

Don’t fall victim to these common TCO pitfalls. Enter the buying process informed about what should – and what shouldn’t—lie ahead in a successful business intelligence implementation and strategy. The right partner is incentivized to ensure you enter into a plan that works best for the unique needs of your company and works with you for a fast return on investment and long-lasting, mutually beneficial relationship.

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Source: insideBigData

Streaming Analytics with StreamAnalytix by Impetus

The insideBIGDATA Guide to Streaming Analytics is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting new area of technology. Many enterprises find themselves at a key inflection point in the big data timeline with respect  to streaming analytics technology. There is a huge opportunity for direct financial and market growth for enterprises by leveraging streaming analytics. Streaming analytics deployments are being engaged by companies in a broad variety of different use cases. The vendor and technology landscape is complex and numerous open source options are mushrooming. It’s important to choose a platform that will supply a proven and  pre-integrated, performance-tuned stack, ease of use, enterprise-class reliability and flexibility to protect the enterprise from rapid technology  changes. Maybe the most important reason to evaluate this technology now is that a company’s competitors are very likely implementing  enterprise-wide real-time streaming analytics right now and may soon gain significant advantages in customer perception & market-share. The complete insideBIGDATA Guide to Streaming Analytics is available for download from the insideBIGDATA White Paper Library.

StreamAnalytix is a state-of-the-art streaming analytics platform based on a best-of-breed open source technology stack. StreamAnalytix is a  horizontal product for comprehensive dataingestionacross industry verticals. It is developed on an enterprise-grade scale with open source components including Apache Kafka, Apache Storm and Apache Spark while also incorporating the popular Hadoop and NoSQL platforms into its structure. The solution provides all required components for streaming app-development not normally found in one place, all brought together under this platform combined with an extremely friendly UI.

A key benefit of StreamAnalytix is the multi-engine abstracted architecture which enables alternative streaming engines underneath—supporting  Spark Streaming for rapid and easy development of realtime streaming analytics applications in addition to original support for Apache Storm. Being  able to choose among multiple streaming engines means you can take the risk out of being constrained with a single engine. With a multiengine streaming analytics platform, you can do Storm streaming pipelines and Spark streaming pipelines and interconnect them—using the best engine for the best use case based on the optimal architecture. When new engines become widely accepted in the future they can be rolled into this multi-engine platform.

The following is an overview of the product and its enterprise-grade, multi-engine open source based platform:

Open source technology

StreamAnalytix is built on Apache Storm and Apache Spark (open source distributed real-time computation systems) and is therefore able to leverage the numerous upgrades, improvements and flow of innovation that are foundational to the global Open Source movement.

Spark streaming

Spark streaming includes a rich array of drag-and-drop Spark data transformations, Spark SQL support, and built-in operators for predictive models with inline model-test feature.

Versatility and comprehensiveness

StreamAnalytix is a “horizontal” product for comprehensive high-speed data-ingestion across industry verticals. Its IDE development environment  offers a palette of applications based on customer requirements. Multiple components can be dragged and dropped into a smart dash-board in order  to create a customized work-sphere. The visual pipeline designer can be used to create, configure and administer complex real-time data pipelines.

Abstraction layer driving simplicity

The platform’s architecture incorporates an abstraction layer beneath the application definition interface. This innovative setup enables automatic selection of the ideal streaming engine while also allowing concurrent use of several engines.

Compatibility

Built on Apache Storm, Apache Spark, Kafka and Hadoop, the StreamAnalytix platform is seamlessly compatible with all Hadoop distributions and vendors. This enables easy ingestion, processing, analysis, storage and visualization of streaming data from any input data source, proactively  boosting split-second decision making.

“Low latency” capability and flexible scalability

The platform’s ability to ingest high-speed streaming data with very low, sub-second latencies makes it ideal for use cases which warrant split-second response, such as flight-alerts or critical control of risk factors prevalent in complex manufacturing environments. Any fast-ingest data store can be used.

Intricate robust analytics

StreamAnalytix offers a wide collection of built-in data-processing operators. These operators enable high-speed data ingestion and processing in  terms of complex correlations, multiple aggregation functions, statistical models and window aggregates. For rapid application development, it is possible to port predictive analytics and machine learning models built in SAS or R via PMML onto real-time data.

Detailed data visualization

StreamAnalytix provides comprehensive support for 360-degree real-time data visualization. This means the system delivers incoming data streams instantaneously in the form of appropriate charts and dashboards.

If you prefer, the complete insideBigData Guide to Streaming Analytics is available for download as a PDF from the insideBIGDATA White Paper Library, courtesy of Impetus.

Source: insideBigData

ODPi Publishes First Runtime Specification and Test Suite To Simplify and Expedite Development of Data-Driven Applications

ODPi, a nonprofit organization accelerating the open ecosystem of big data solutions, announced the first release of the ODPi Runtime Specification and test suite to ensure applications will work across multiple Apache Hadoop^® distributions.

Designed to make it easier to create big data solutions and data-driven applications, the ODPi Runtime Specification is the first release from the industry-backed organization. While the Hadoop ecosystem is rapidly innovating, a certain degree of diversity and complexity are actually impeding adoption. Founded last year, more than 25 ODPi members are focused on simplification and standardization within the big data ecosystem and further advancing the work of the Apache Software Foundation.

Descending from Apache Hadoop 2.7, the Runtime Specification features HDFS, YARN, and MapReduce components and is part of the common reference platform ODPi Core.

The turbulent big data market needs more confidence, more maturity, and less friction for both technology vendors and consumers alike,” said Nik Rouda, senior big data analyst at Enterprise Strategy Group (ESG). “ESG research found that 85% of those responsible for current Hadoop deployments believed that ODPi would add value.”

Key ODPi Runtime Specification Technical Features

The ODPi test framework and self-certification also aligns closely with the Apache Software Foundation by leveraging Apache BigTop for comprehensive packaging, testing, and configuration. Additionally, more than half the code in the latest Big Top release originated in ODPi.

All ODPi Runtime-Compliance tests are linked directly to lines in the ODPi Runtime Specification. To assist with compliance, in addition to the test suite, ODPi also provides a reference build.

The published specification also includes rules and guidelines on how to incorporate additional, non-breaking features, which are allowed provided source code is made available through relevant Apache community processes.

What’s Next for ODPi

The ODPi Operations Specification to help enterprises improve installation and management of Hadoop and Hadoop-based applications will be available later this year.  The Operations Specification covers Apache Ambari, the ASF project for provisioning, managing, and monitoring Apache Hadoop clusters.

ODPi complements the work done in the Apache projects by filling a gap in the big data community in bringing together all members of the Hadoop ecosystem,” said John Mertic, senior manager of ODPi. “Our members – Hadoop distros, app vendors, solution providers, and end-users – are fully committed to leveraging Apache projects and utilizing feedback from real-world use cases to provide industry guidance on how Hadoop should be deployed, configured, and managed. We will continue to expand and contribute to innovation happening inside the Hadoop ecosystem.”

Comments from Members

Ampool

With its broader, flexible approach to standardizing the Hadoop stack, ODPi is particularly attractive to smaller companies, such as Ampool. Instead of spending testing/qualification cycles across different distributions and respective versions, the reference implementation would really help reduce both the effort and risk of Hadoop integration for us.” – Milind Bhandarkar, Ph.D, founder and CEO, Ampool

DataTorrent

ODPi will simplify developing and testing applications that work across distros and hence lower the cost of building Hadoop-based big data applications. For example, DataTorrent will be able to certify RTS installation and runtime for ODPi and know it will work with multiple platform providers.” – Thomas Weise, Apache Apex (incubating) PPMC member and architect/co-founder, DataTorrent

Hortonworks

At Hortonworks, we aim to speed Hadoop adoption through ecosystem interoperability rooted in open source so enterprise customers can reap the benefits of increased choice with more modern data applications and solutions. As a founding member, we are pleased to see ODPi’s first release become available to the ecosystem and look forward to our continued involvement to accelerate the adoption of modern data applications.” – Alan Gates, co-founder, Hortonworks

IBM

Big Data is the key to enterprises welcoming the cognitive era and there’s a need across the board for advancements in the Hadoop ecosystem to ensure companies can get the most out of their deployments in the most efficient ways possible. With the ODPi Runtime Specification, developers can write their application once and run it across a variety of distributions – ensuring more efficient applications that can generate the insights necessary for business change.” – Rob Thomas, vice president of product development, IBM Analytics

Linaro

Linaro recognizes the importance of ODPi’s work to promote and advance the state of Apache Hadoop and Big Data technologies for the enterprise while minimizing fragmentation and redundant effort. Linaro’s own focus is similar to this in developing open source software for the ARM ecosystem and it makes perfect sense that where these two areas intersect that Linaro and ODPi should work together to ensure ARM is fully supported and that fragmentation is minimized across the industry.” – Martin Stadtler, director of the Linaro Enterprise Group (LEG)

Pivotal

It was a little over a year ago that ODPi was formed, and we have already proved beneficial to upstream ASF projects (Hadoop, Bigtop, Ambari). There’s a need for a stable enterprise-grade platform that is managed as an industry asset to benefit all of the companies driving value from Hadoop and big data. This is why the first release of the ODPi Runtime Specification and test suite is so exciting. It is a big step toward realizing our goal of accelerating the delivery of business outcomes through big data solutions by driving interoperability on an enterprise-ready core platform.” – Roman Shaposhnik, director of Open Source at Pivotal, Apache Hadoop and Bigtop committer and ASF member

SAS

As a founding member, SAS’s support of the Open Data Platform Initiative demonstrates our ongoing commitment to developing innovative applications and solutions for our customers that are compatible with the Hadoop ecosystem. OPDi enables us to remain committed to ensuring our applications work with and exploit the Hadoop distribution of our customers’ choice, while being able to bank on the stability and quality expected in demanding business environments.” – Craig Rubendall, vice president of platform R&D, SAS

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Source: insideBigData

Paxata Continues to Redefine the Traditional Data-to-Information Pipeline with New Spring ‘16 Release

Paxata, provider of the Adaptive Data Preparation™ platform for the enterprise, announced the availability of its Spring ’16 product release. Paxata’s latest release bridges the gap between analysts and IT with new intuitive capabilities, providing connected information to every person in the enterprise without compromising on security, scale, and cost efficiency. Spring ’16 also enables analysts to collaboratively explore and prepare all of their data no matter the source or format.

Our investigations involve a great deal of unknowns in the data, and our customers turn to us to make sense of it,” said Conrad Mulcahy, Associate Managing Director and Director of Data Analytics, K2 Intelligence. “Paxata’s Spring ’16 release allows K2 to do a highly sophisticated MRI on the data. Paxata already showed us hard tissue versus soft tissue, but now we can distinguish between different kinds of soft tissue. Granular observations can be made on the data at an early stage with all of the new capabilities: sophisticated sampling options, cluster and edit, column search and support for nested files. Paxata keeps us from going in the wrong direction early on, keeps us focused, and gets the dialogue with the client headed in the right direction. It’s hard to put a price on how valuable that is for us as investigators, having our clients know that we’re not wasting their valuable time or resources.”

Paxata’s new release serves as another milestone in Paxata’s mission of delivering connected information to every person in the enterprise, without compromising on security, scale, and cost efficiency. Key features of Paxata’s Spring ’16 release includes:

• Advanced filtergrams for comprehensive data profiling with semantic-awareness of timestamp and numeric data, automatically suggested intelligent visualizations and custom bucketing
• Smart integration of complex nested JSON/XML data and Hadoop compressed files – unfolded, flattened and ready for multi-structured data analysis to address IoT and other high-value use cases
• Granular searching across all columns of wide datasets and in every cell value for patterns, outliers and duplicate values
• New options for iterative and flexible data discovery with smart statistical selections of datasets at any scale

Cloudera is committed to advancing Hadoop as a mainstream platform that improves customer experiences and drives new revenue streams through highly scalable, more intelligent storage and processing capabilities,” said Tim Stevens, vice president, Business and Corporate Development at Cloudera. “Paxata continues to deliver on the promise of the Hadoop ecosystem with numerous joint customers who have amplified the benefits of their Cloudera platform by making it accessible through Paxata’s connected information platform for self-service data quality, integration, governance and collaboration.”

In addition to providing quick access to data, the new release provides IT-specific controls to support governance, security and scale, including:

• Visual column-lineage for detailed and understandable traceability
• REST API for SAML for complete integration into the IT environment
• Ability to use analyst projects as repeatable “recipes” to build into ETL, virtualized views or data quality dashboards

Since we began the self-service data preparation revolution, we set the pace for delivering major advancements against our roadmap. With every quarterly release, we ask two questions, the first being ‘how do we make the life of the analyst easier so they can go from raw data to the right information regardless of analytic use case?’” The second is ‘how do we lead the industry in moving from legacy scale-up, on-premise, relational worlds to distributed, elastic cloud, scale-out architectures?’” said Prakash Nanduri, Co-Founder and CEO of Paxata. “Every major Fortune 1000 corporation is moving to this new world and Paxata is leading the way. The Spring ’16 release is another major advancement in this transformation. I am proud of the hard work of our team, customers and partners.”

Additional details about the Paxata Spring ’16 release can be found HERE.

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Source: insideBigData

Manage Supercharges Its Demand-Side Platform for Mobile Advertising With Aerospike NoSQL Database

Aerospike, the high-performance NoSQL database company recognized for “speed at scale” leadership and as the NoSQL leader in the digital media and ad tech industries, announced that Manage.com Group Inc. (Manage) has selected Aerospike to power its innovative demand-side platform for mobile advertising. Manage chose Aerospike to support its goals for technology evolution and business growth.

Industry analysts expect mobile ad spend to exceed $65 Billion by 2019, with nearly 50 percent of that revenue generated by display ads.1 This growth will be fueled by the ability of companies like Manage to deliver fast, data-driven solutions that bring mobile advertisers and publishers closer to mobile consumers.

We needed a no-compromise database with speed, scalability, reliability and economy,” said Kai Sung, CTO at Manage. “Aerospike delivers on all counts with a fast key-value store and database infrastructure that allows us to evolve and grow our platform, without the need for a caching system.”

Manage provides fully managed programmatic mobile advertising and real-time bidding (RTB) capabilities in a demand-side platform (DSP) that relies on a distributed database infrastructure with clusters in the U.S., Europe and the Asia Pacific region. Manage requires high availability, automatic failover, efficient scalability and cross data center replication (XDR) to meet the growing needs of its customers. When the company’s previous database solution could no longer keep pace with business demands for scalability and high availability, its development team evaluated several alternatives and adopted Aerospike based on its performance and price advantages. Manage processes more than 40 billion programmatic bid requests every day and handles up to 500,000 queries per second at peak hours. Manage needs to meet a Service Level Agreement (SLA) driven by business requirements of 100 milliseconds for each bid request, and every bid request is made up of many different sub-steps — each of which has its own SLA. For the database it has an SLA of 10 milliseconds. Aerospike routinely provides sub-2 millisecond latency, enabling Manage to meet its business SLA and provide enhanced RTB capabilities for its customers.

Manage is serious about both the performance of its mobile DSP and operational efficiencies behind the scenes,” said Brian Bulkowski, CTO and co-founder at Aerospike. “Their developers are leveraging Aerospike capabilities to meet SLAs and better utilize stored data while reducing manual tasks and storage costs. We’re proud to be an enabling technology partner in the company’s success.”

Through its partnership with Aerospike, Manage has increased its storage capacity by 10x to store more user profile and customer segment data, which it uses to enhance campaign optimization. The faster processing speed of Aerospike’s SSD-based NoSQL database allows Manage to obtain a more accurate real-time calculation of a user’s value for each impression and advertiser, which enables the company to bid more efficiently and price bids more appropriately — ultimately increasing its win-rate for customers. With Aerospike, Manage is able to:

• Consistently deliver a sub-2 millisecond database SLA, which in turn enables it to meet its business SLA
• Store 1 billion rich user profiles
• Process 400,000 writes and 300,000 reads per second
• Boost storage capacity by 10x with cost savings
• Automate cluster management, failover and replication
• Ensure zero downtime

Aerospike is the backbone for storing our user data in the smartest, most efficient and reliable way,” said Sung. “By enabling our expansion to one billion robust user profiles and leveraging the increased richness of these stored profiles, Aerospike gives us a competitive advantage. Now we can bid with more precision and efficiency, price bids more appropriately, and win more inventory.”

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Source: insideBigData