Joshua Siegel – InFocus Blog | Dell EMC Services https://infocus.dellemc.com DELL EMC Global Services Blog Tue, 07 Aug 2018 19:04:52 +0000 en-US hourly 1 https://wordpress.org/?v=4.9.7 How Big Data Will Transform Business Models in the Legal Industry https://infocus.dellemc.com/j_siegel/big-data-transform-legal-industry/ https://infocus.dellemc.com/j_siegel/big-data-transform-legal-industry/#respond Tue, 12 Sep 2017 09:00:48 +0000 https://infocus.dellemc.com/?p=32330 Co-written by Matthew Colon, Senior Consultant Big Data & IoT Consulting Dell EMC For those of you of a certain age, you may remember “Class Action,” the 1991 movie with Gene Hackman and Mary Elizabeth Mastroantonio. Loosely based on the Ford Pinto case, it follows a small law firm (led by Gene Hackman) in its fight […]

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Co-written by Matthew Colon, Senior Consultant Big Data & IoT Consulting Dell EMC

For those of you of a certain age, you may remember “Class Action,” the 1991 movie with Gene Hackman and Mary Elizabeth Mastroantonio. Loosely based on the Ford Pinto case, it follows a small law firm (led by Gene Hackman) in its fight against big business attempting to keep a central fact hidden – that they knew about a fatal flaw in a car after it went to market.

There is a scene where the larger law firm representing the car manufacturer purposely misfiles a crucial piece of damning evidence in the discovery process. The smaller law firm searches in vain for the missing piece of evidence – hopelessly sorting through box after box of paper. In the end, only an attack of conscience from one of the corporate attorneys leads to the missing evidence and justice for those injured by the car defect.

Today, this scenario is much less likely to happen. With ubiquitous e-Discovery tools like kCura, Lexis Nexis and MS SharePoint among others, lawyers and legal investigators have much better access to digitized records and natural language search. If that missing “exploding gas tank” memo is there – chances are much better it will be found.

Providing evidence to opposing counsel (the “discovery” process) is only a small component of practicing law. Legal professionals in fact spend relatively little time in the courtroom and more time on contracts, torts, employee issues, mergers and acquisitions, etc. And while tools to help with e-Discovery are fairly mature, analytics and computer-aided techniques are only beginning to be applied to other areas of the law. The opportunities are massive, but only if law firms take a comprehensive approach. Think about the other areas of the legal profession (lumping all sorts of legal sub-specialties for the moment) and how getting more access to data and insights can be applied to improve outcomes for clients. Capabilities like:

  • Case law and precedent research
  • Predictive opinion or outcome research
  • Real time verdict/judgment analytics

But analytics can also be applied for the law firm’s own benefit:

  • Identifying and on-boarding new clients
  • Cross-selling and upselling existing clients
  • Right-sizing firms, SMEs, and identifying the appropriate leverage model
  • Reducing operational costs

Law firms appear to be leveraging point solutions for client-facing improvements (e.g., Ravel Law, Lex Machina, Ross Intelligence); but there still exists tremendous opportunity to take a more comprehensive approach to data analytics – specifically by leveraging data to improve law firm profitability and operations.

Competition Makes Data Analytics Mandatory

Law firms are struggling. In its recent 2016 Chief Legal Officer Survey[1], the consulting firm Altman Weil reported that, among the 336 corporate CLOs responding to its survey, 35.2% planned to decrease their spend on outside counsel during the coming 12 months.

I-fought_the_law_pic_1

 

In a similar report, the Georgetown University Center for the Study of the Legal Profession[2], notes that firms are increasingly looking to alternative vendors to attend to pieces of previously bundled legal services, resulting in stagnating demand growth.

 

I-fought_the_law_pic_2

 

Other examples of the changing legal landscape include companies like Legal Zoom which is leading to the increasing offshoring and “Amazon-ification” of the law industry. Likewise, Artificial Intelligence (AI) is replacing thousands of lawyers at JP Morgan Chase[3] and elsewhere. This is a huge shot across the bow for the legal industry. JPMC replaced roughly 360,000 billable hours – at a conservative estimate of $200/hour, someone just lost $72m in legal fees…

Given these examples and countless others that together showcase (1) increases in low-cost competition, (2) replacement of humans with artificial intelligence and (3) the decreasing willingness of large companies to pay the way they have, the stage is set for law firms to either adapt to or face the consequences of these trends.

 

I-fought_the_law_pic_3

 

Adapting

Rather than implementing one-off solutions over time, law firms – like every other industry facing disruption in today’s economy – need to leverage their single best source of competitive advantage to compete: the data they have on their clients and their historic operations. Only by improving services, being proactive and providing more value, can law firms adapt and thrive.

So how can law firms get more proactive?

Leverage data and the insights it provides to become more valuable to their clients:

  • Leverage unique and proprietary data – case notes, call center records, legal strategies, leverage models, practice profitability, resource expertise profiles
  • Develop and deploy AI / automated solutions for clients before they deploy them for themselves
  • Incorporate new, publically available and innovative data sources that will add value to clients (Product recalls? Consumer Comments? Social media comments? Construction permits? Road maintenance? Weather?)

The possibilities are endless…

Law firms should look at specific metrics to improve by accessing and leveraging data. Examples include:

Improvable Metrics

  • Billable hours / day
  • Partner / Practice profitability
  • Cross-sell / Up-sell
  • New lawyer recruitment
  • Proactive practice management – what law is growing, what’s shrinking
  • Identifying lawyers who aren’t producing
  • Leverage model
  • Fee structures
  • New client intake

Leveragable Data Sets

  • Case records
  • Public records
  • Time & Billing records
  • CRM data
  • HR data
  • Demographic / education / recruiting
  • Call center records

Matters / Outcomes

  • Better management of operational costs
  • More efficient resource and SME management
  • Identification of new work / new client opportunities
  • Expedited strategy analysis (case law, precedent, opinion or outcome research)
  • Real-time verdict / judgment analytics

Law firms that invest in holistic data management, storage and analytics infrastructure can identify and take advantage of insights that firms without that technology (or with only point solutions) cannot. The technologies that enable access to this data are relatively cheap to acquire and maintain – and are also available as-a-service in the cloud. Once the technology is in place, law firms that identify and prioritize use cases and drive real ROI will start to improve outcomes for clients and operational metrics for the firm.

The Future of Law

The legal profession remains vastly underpenetrated when it comes to leveraging big data technologies and processes. Operating a law firm can and should still be a profitable business – but that business is changing. Banks are technology companies that happen to manage money. Law firms will have to learn they are technology companies that happen to practice law. They need to put their assets to work to maintain, or in some cases, re-establish their value with clients.

Law firms that invest in holistic data management, storage and analytics infrastructure can identify and take advantage of insights that firms without that technology (or with only point solutions) cannot. The technologies that enable access to this data are relatively cheap to acquire and maintain – and are also available as-a-service in the cloud. Once the technology is in place, law firms that identify and prioritize use cases and drive real ROI will start to improve outcomes for clients and operational metrics for the firm.

[1] Altman Weil 2016 Chief Legal Officer Survey

[2] Georgetown University Center for the Study of the Legal Profession

[3] JP Morgan Chase

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Disruption on the Doorstep: 3 Essential Components to Moving Forward with Digital Transformation for Large Enterprises https://infocus.dellemc.com/j_siegel/disruption-doorstep-3-components-digital-transformation/ https://infocus.dellemc.com/j_siegel/disruption-doorstep-3-components-digital-transformation/#respond Tue, 20 Jun 2017 09:00:23 +0000 https://infocus.dellemc.com/?p=31484 Established businesses – and indeed entire business models – are at risk of disruption due to digital transformation. New entrants, with new innovative business models, are upending industries such as transportation, manufacturing, financial services and communications, to name just a few – with many more on the horizon. Given the pace and impact of digital […]

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Established businesses – and indeed entire business models – are at risk of disruption due to digital transformation. New entrants, with new innovative business models, are upending industries such as transportation, manufacturing, financial services and communications, to name just a few – with many more on the horizon. Given the pace and impact of digital disruption, the term “Proven business model” is becoming an oxy-moron. Businesses cannot base decisions on what has worked in the past – they must re-evaluate everything in light of today’s digital, global economy. Everything: every customer touchpoint, sales cycle, business model and data source. In fact, some companies would be wise to consider how to START OVER.

Younger organizations have no legacy processes or infrastructure, so they don’t have to START OVER – they just have to START. Large enterprises may have deep pockets, but struggle to take advantage of digital transformation rather than become displaced by it. This requires assessing and re-evaluating  the potential value of one’s digital and data assets. Unfortunately, few understand the process to do that, and the consequences are happening all around us. More than half of the Fortune 500 have disappeared since year 2000!

Among the challenges large organizations face are: how to (1) measure and value the enterprise in light of digital transformation, (2) how to monetize that value and finally (3) how to minimize new and existing risks to that business.

  1. Measuring Value – GAAP does not and cannot currently accurately account for digital transformation. Assets by definition have utility.  But GAAP does not (in most cases) consider data, algorithms or models as assets – as opposed to more traditional assets such as physical plant or inventory. This model is inadequate because data has a unique property, in that, its value is increased every time it’s used – and, it doesn’t get consumed in the process. Companies need specific guidance and methodologies to both initially assess and keep this valuation up to date. My colleague Bill Schmarzo, CTO of Dell EMC’s Big Data Consulting practice, has a great perspective here: https://infocus.dellemc.com/william_schmarzo/economic-value-data-challenges/
  2. Maximizing Value – invariably, once the measuring is done, use cases are uncovered, and value calculated, new opportunities emerge. Data assets are often severely undervalued as it relates to potential value they can add (in terms of revenue, processes efficiency, cost reduction, risk management, etc.). Monetizing these data sources the key to unlocking the potential of the big data era. See @Schmarzo for excellent white paper on the economic value of data: https://infocus.dellemc.com/wp-content/uploads/2017/04/USF_The_Economics_of_Data_and_Analytics-Final3.pdf
  3. Minimizing Risk – Understanding the unlocked potential in data sources and models is critical to digital transformation. Once that value is fully understood, companies can and should reflect on how those digital assets are protected and insured. For example, a company may insure its inventory, physical assets or key executives’ lives, but many organizations do not internalize that data itself is an asset that must be secured and insured. Denial of access to data such as we recently saw with the global wannacry cyberattack, is just one example of the risk inherent in underappreciating reliance on data. It has both present and future value – and only once that value is fully understood can the risk be mitigated.

Commercial enterprises that follow these three steps can accelerate their transformation journey. The threat of disruption can be your biggest fear, or your most powerful weapon. Your move.

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Hadoop is Just the Beginning: Realizing value from big data requires organizational change – and it’s hard. https://infocus.dellemc.com/j_siegel/hadoop-just-beginning-realizing-value-big-data-requires-organizational-change-hard/ https://infocus.dellemc.com/j_siegel/hadoop-just-beginning-realizing-value-big-data-requires-organizational-change-hard/#comments Wed, 30 Nov 2016 16:50:56 +0000 https://infocus.dellemc.com/?p=29570 Back in the 1990s, ‘decision science’ was all the rage. Really the harbinger of big data, decision science focused on streamlining decision-making and using all available tools and data for advanced modeling. Consolidating and combining disparate, independent functions became a key enabler of decision science. For example: Marketing financial services offerings, if done independently of […]

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Back in the 1990s, ‘decision science’ was all the rage. Really the harbinger of big data, decision science focused on streamlining decision-making and using all available tools and data for advanced modeling. Consolidating and combining disparate, independent functions became a key enabler of decision science. For example: Marketing financial services offerings, if done independently of a Risk Management function, focused mostly on increasing revenue from new accounts. Risk Management, however, also needed to ensure that those new accounts would not ultimately become bad assets. Combining elements of both functions allowed for a more efficient, coordinated process, with better outcomes.

Decision Science, in the example above, was most effective when not just the analytic models were combined, but the organizations as well; the most effective companies employing decision science created new organizations, roles and titles as well as processes focused on coordination and control.

Twenty five years later, decision science is replaced with ‘Data Science.’ Essentially the same concept: deploying better solutions through advanced data access and modeling; except now the data is at massive scale. Companies are deploying new technologies at a record pace, but many of those same companies are neglecting to update organizationally as they would have with decision science because it can be very hard to do: It’s one thing to bring on new technologies, but updating organizations, moving resources around, changing reporting relationships… that’s hard! The result, however, not just inhibits, but actually prohibits change. The real value from big data is not accruing as it should.

To be ultimately effective, big data technology relies on five core enablers:

  1. Use case generation, prioritization and approval – links business value to analytics initiatives
  2. Data lineage and metadata – ties use case implementation to core data assets
  3. Governance, security & access control – ensures regulatory compliance
  4. Success tracking & learning incorporation – enables learning, evolution, and business transformation
  5. Operating model & budget allocation – tackles the often thorny question: who gets control

Simply implementing policies or technologies to install these functions is not enough; the organization must be set up to embody and embrace these core principles. That doesn’t happen without making some hard decisions:

  • If the CxO pays for the infrastructure, does the CxO decide what use cases get deployed and in what order? If not, who does and why?
  • How do analytic models get deployed into production? What controls exist to review and refine the results?
  • On what source systems and data are use cases deployed? Is source system sprawl simply getting covered over or are real data issues being fixed in the process?
  • Who will data lake administrators report to? Data scientists? Does every organization have their own, or are they centralized?

Without tackling and answering these fundamental questions along with deployment of new technology, that technology will not produce anything close to lasting, transformative change.

So how do leading organizations tackle this? How do they make sure that they have a transformed organization to support a transformed big data capability?

To affect these functions, consider two governance bodies: an Analytics Governance Council (AGC) and a Data Governance Council (DGC). These two bodies are made up of current stakeholders and are designed to seamlessly and collaboratively govern a data lake and analytics capability.   Here’s how they look and what they do:

Analytics Governance Council (typically up to 12 members)

  • Agrees on use case prioritization methodology
  • Audits potential use cases to pursue for best combination of implementation feasibility and business benefit
  • Recommends BI tool disposition
  • Oversees and approves DGC
  • Sets data lake / DaaS permission levels for Data Scientists
  • Works to secure approval for use case implementation
  • Meets monthly
  • Made up of senior executives with budgetary authority representing business units or functions from the business

Data Governance Council (no more than 10-12 members)

  • Operates at an operational level – decides on specific data components underlying use cases
  • Defines and approves metadata labels
  • Organizes and rationalizes data sources
  • Advises on access to data and potential compliance issues
  • Sets standard data sources, tables, elements to be used for each use case / calculation
  • Meets weekly (initially)
  • Made up of data stewards or SMEs with direct knowledge of core data sources on which use cases are built

organizational change

There are real nuances in how these bodies are constructed and in what they do. If done right, however, these constructs allow for real decisions to be made with shared accountability for the results.

They enable the organizational change that must underpin the rapid advances in big data and data science. Without them, the highly touted and much promised step change in productivity, revenue growth and customer experience resulting from big data and advanced analytics cannot accrue.

For more information on how we’ve helped our customers navigate these changes and develop big data and analytics into a formal business practice please visit http://www.dellemc.com/bigdataservices

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Analytic Insights Module from Dell EMC: Batteries Included and No Assembly Required https://infocus.dellemc.com/j_siegel/analytic-insights-module/ https://infocus.dellemc.com/j_siegel/analytic-insights-module/#respond Wed, 19 Oct 2016 14:00:26 +0000 https://infocus.dellemc.com/?p=29220 If you have kids (or buy presents for kids!), you understand the value of a present that comes ready to use – no need to buy batteries, no assembly required, no complex packaging, no need to download and install the “app”, no need to charge it for 12 hours before your you can use it, […]

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If you have kids (or buy presents for kids!), you understand the value of a present that comes ready to use – no need to buy batteries, no assembly required, no complex packaging, no need to download and install the “app”, no need to charge it for 12 hours before your you can use it, and no need to read pages of poorly translated instructions. Dell EMC has developed just such a product for organizations that need predictive, transformative business analytics right away: fully-engineered, configured, with integrated technology to simplify the ecosystem across the data analytics life cycle!

Analytic Insights Module from Dell EMC combines self-service data analytics with cloud-native application development into a single cloud platform. It allows data analytics teams to produce analytic insights while reducing both time-to-value and the cost of operationalizing those insights. Analytic Insights Module infrastructure provides for massive efficiencies over ‘do-it-yourself’ options – with an integrated data lake (Dell EMC Isilon or Dell EMC ECS) and Pivotal Cloud Foundry (delivered via Native Hybrid Cloud) along with centralized support. The design delivers industry-leading access control and security, both rapid and controlled data ingestion and governance, as well as user-friendly provisioning of data. Analytic Insights Module is capable of supporting a whole host of applications and business processes ranging from electronic fraud detection and customer experience optimization, to predictive machine maintenance.

While a fully integrated infrastructure and architecture are critical components of achieving business success with data analytics, equally (or more) important is ensuring that there are business-supported, ROI-producing use cases to deploy on that infrastructure. To that end, Analytic Insights Module is supported with strategic consulting services to ensure that these go hand-in-hand.

While an Analytic Insights Module deployment is designed end-to-end to support multiple business outcomes through analytics, many Dell EMC customers choose to take advantage of Dell EMC’s Big Data Consulting offerings before, during, and/or after deployment to help businesses reach their goals faster. These services employ our industry-leading big data consultants and data scientists, to align IT and the business around goals for analytics, validate specific use cases and architecture choices and ensure comprehensive support for an Analytic Insights Module deployment. Dell EMC Professional Services also builds out and deploys functioning, effective analytics use cases into production, with appropriate organizational governance.

Think of Analytic Insights Module as a great holiday present: delivered to your doorstep, unwrapped, fully assembled and tested, with batteries included and instructions provided.  Standard installation services that come with Analytic Insights Module teach you how to use your new toy.  Dell EMC consulting experts make sure you know how to do wheelies and stunts – how to impress and out-compete your neighbors – all while keeping your company’s data safe.

Here are just some of the capabilities Dell EMC’s Big Data Consulting brings to the table:

Pre deployment Services:

  • Build organizational consensus around the need to move into the big data / IOT space
  • Confirm and prioritize use cases
  • Link business & IT goals
  • Showcase illustrative big data analytics and statistical techniques
  • Prove the value in a use case: A/B testing, business case construction, results tracking

During deployment Services:

  • Design and deploy a governance model for how Analytic Insights module gets used
  • Review data sources; build semantic maps
  • Customize Data-as-a-Service capability and deploy a data catalogue
  • Build and deploy advanced data-science driven use cases
  • Execute analytic insights process: moving from development to test to production

Post deployment Services:

  • Data lake administration services
  • On site or offshore Data Scientist support for advanced analytics
  • Data and organizational governance
  • Program management support

For most Analytic Insights Module customers, it is critically important to derive value as soon as possible.  That value – in the form of measurable results for use cases such as fraud reduction, cost savings or revenue enhancement – helps make the case for change and continued growth around an analytic practice with the organization. A couple great examples of Dell EMC Big Data consulting support from recent customers include:

  • Documented, consolidated and prioritized 60 use cases for a large credit card issuer to set the analytics priorities across the organization
  • Created and executed a marketing campaign to enhance individual guest experiences utilizing predictive analytics for a large resort & casino.
  • Deployed data governance and semantic mapping for several large hospital chains; helping to ensure they get full value out of service providers and datasets
  • Designed and operated a governance process for multiple customers to assign priority to planned analytics use cases and deployments

Check out this short, 2 minute video where the CIO, CFO and CMO of a recent Analytic Insights Module customer discuss the importance of Consulting Services:

Pechanga Resort and Casino

Get the most out of the Analytics Insight Module by leveraging Dell EMC’s Big Data Consulting Services.  Let us help you drive business value and scale with both business and IT support.  Let Dell EMC Big Data Consulting help you get the most out of your deployment!  For more information on Analytic Insights Module value-added Services, please contact your Dell EMC representative and visit: www.dellemc.com/aim.

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Big Data Transformation: Pechanga Resort and Casino https://infocus.dellemc.com/j_siegel/pechanga-resort-casinos-big-data-transformation/ https://infocus.dellemc.com/j_siegel/pechanga-resort-casinos-big-data-transformation/#respond Tue, 30 Aug 2016 09:00:52 +0000 https://infocus.dellemc.com/?p=27143 Pechanga, California’s largest resort and casino, started its journey with EMC late in 2014 with a Big Data Vision Workshop. The engagement illustrated how Pechanga could most effectively utilize analytics and build consensus around moving forward with a larger scale test of Big Data analytics. In early 2015, Pechanga moved on to the next phase […]

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Pechanga, California’s largest resort and casino, started its journey with EMC late in 2014 with a Big Data Vision Workshop. The engagement illustrated how Pechanga could most effectively utilize analytics and build consensus around moving forward with a larger scale test of Big Data analytics.

In early 2015, Pechanga moved on to the next phase of EMC’s Big Data Transformation methodology: the Proof of Value. Over the course of 16 weeks, EMC professionals built and tested specific use cases including optimizing ‘comps,’ hotel room stays, and building an advanced gamer profile, created a business case and iterated on a future state architecture. By demonstrating the analytic lift and potential value of specific use cases, Pechanga’s Board of Directors was persuaded to implement the recommended architecture and deploy the analytics use case into production.

Our team spent the last six months of 2015 focusing on that implementation – and it was a true transformation. It included NOT JUST a Business Data Lake, but everything needed to enable a successful business transformation as well: use case deployment, business process improvement, knowledge transfer, data governance, new IT processes and a new IT organization.

Pechange VideoPechanga has already achieved a return on its investment, positioned itself as a leader in its space, and implemented a platform on which it can execute countless additional Big Data analytics use cases based on its customer, operational, and retail data.

We’re especially proud of our work at Pechanga. It exemplifies the success we can have when we approach our customers’ transformation holistically – and when we work together to design and deploy the best solution for our customers.

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With Big Data, Focus on the Journey – Not Just the Destination https://infocus.dellemc.com/j_siegel/with-big-data-focus-on-the-journey-not-just-the-destination/ https://infocus.dellemc.com/j_siegel/with-big-data-focus-on-the-journey-not-just-the-destination/#respond Tue, 28 Apr 2015 15:00:18 +0000 https://infocus.dellemc.com/?p=23467 If you work in a big company – sometimes even a small one – chances are your first day on the job was overwhelming.  You may have gotten very little training, no real sense of who works for whom or why certain people do what they do.  You probably sent the wrong email to the […]

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If you work in a big company – sometimes even a small one – chances are your first day on the job was overwhelming.  You may have gotten very little training, no real sense of who works for whom or why certain people do what they do.  You probably sent the wrong email to the wrong person at some point, or failed to realize the politics of a situation – endangering a project, a goal or even your career.

It’s hard to manage relationships and company politics on a day-to-day basis; imagine how complicated that becomes when you try to do something truly transformative such as bring in big data capabilities and use them to drive business results. Half the people from whom you’d be trying to get buy-in probably still don’t know what big data means!

These organizational and cultural impediments can sink big data initiatives – sometimes before they start and usually before they’re over.  In fact, Gartner predicts 90% of data lakes will be largely useless by 2018 due to uncertain use cases.1

Large organizations often have multiple stakeholders with a range of key considerations around big data infrastructure, governance, storage, analytics, data science, training, etc. Some of these stakeholders, more often than not, have the power to end big data initiatives by withholding funding, or simply through inattention (and you may not even know who they are). 

InF

It is increasingly clear that simply moving forward with big data infrastructure, such as a data lake, needs to be well coordinated among both Business and IT stakeholders to build consensus and buy-in around the end result, as Bill Schmarzo explains in his recent blog EMC World 2015: Achieving Big Data Maturity.  Too often IT departments work in a vacuum – disconnected from the business and ultimate end-users.  This leads to a data lake driven by, and designed to solve, technical challenges, not business challenges.

That’s why Federation Business Data Lake is aptly named.  Not only does it deliver the technical capabilities in an accelerated way to execute on operational use cases that reduce IT cost and streamline operations, it comes with professional transformation and implementation assistance from EMC Professional Services to ensure that the proper prioritization is given to the “business” use cases also.  These Transformation Professionals focus on making sure the big data journey is successful by:

  • Identifying and aligning business and IT stakeholders
  • Ensuring business problems are solved by the technology being implemented
  • Documenting real, incremental ROI
  • Providing a transformation roadmap to become a 21st Century IT organization with the right skillsets and capabilities to drive real change

Without this focus on the massive changes required of both IT and the Business (people, process, and technology), a data lake may ultimately sit under-utilized and under-appreciated.  If you focus on the journey – on bringing the organization along with you on your big data journey – you will have a truly effective big data environment driving real change.  That’s transformation everyone wants to support.

1 “Predicts 2015: Big Data Challenges Move From Technology to the Organization” 28 November 2014; Gartner, Inc.

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