The year2017: The attack of the data silos in an enterprise near you. The data in the enterprises are getting more and morehumongous data in size and these sizes are making the data silos more and morecomplex . for thwarting this data attacks now companies are taking agile stepsto keep these complex data problems at bay by investing in advanced cloud-basedplatforms services. let’s see theGartner report statistics of the enterprise’s investment in the cloud platforms& services. Cloud ApplicationServices (SaaS, $M)2016 38,5672017 46,3312018 55,1432019 64,8702020 75,734 The growth of the public cloud and Saas, Paas, and Iaas. isno news in 2017.
Per a recent Gartner report (http://www.gartner.com/newsroom/id/3616417)”As enterprise application buyers are moving toward a cloud-first intellect, weestimate that more than 50 percent of new 2017 large-enterprise North Americanapplication adoptions will be comprised of SaaS, Paas and Iaas like cloud-basedsolutions. Midmarket and small organizations are even further on the adoptiontrajectory.
By 2019, more than 30 percent of the 100 largest merchants’ newsystems priority will have changed from cloud-first to cloud-only.” As enterprises adopt a pantheon of SaaS apps, so raises therisk of having isolated lake of data each trapped under its own SaaS serviceand creating yet another data silo. The inherent plug-and-play convenience ofSaaS applications often means that these systems are built to handle an uprightbusiness use case without producing sufficient leisure to integrate into theexisting enterprise application estate. Often, the low cost of service and fasttime to deployment makes it extraordinarily convenient to get theseapplications up and running for every use case and for every department.
All ofa sudden information on a business entity, such as a customer, is into a dozendiverse and disconnected places. Now, data integration across marts has been a problem sincepast unknown. Organizations have long seen the value in aggregating data frommultiple systems and channels into an individual, holistic, real-timerepresentation of a business entity or domain.
However, for many organizations,successfully achieving a single view has been elusive. Technology has certainlybeen a limitation – for example, the hard, tabular data model imposed bytraditional relational databases hinders the schema flexibility necessary toaccommodate the various data sets held in source systems. But constraintsextend beyond just the technology to include the business processes needed todeliver and maintain a single view.The single view is relevant to any industry and domain as itaddresses the generic problem of managing disconnected and duplicate data.
Specifically,a single view solution does the following: • Gathers and organizes data from various, disconnectedsources;• Sums information into a patterned format and jointknowledge model;• Gives holistic pictures of connected applications orservices, across any digital channel;• Helps as a base for analytics – for instance, customercross-sell, upsell, and churn risk. From scoping to building to operationalization, a successfulsingle view project is founded on a structured approach to solution delivery.Identify a repeatable, 10-step methodology and toolchain that can move anenterprise from its current state of siloed data into a real-time single viewthat improves business visibility.
The timescale for each step is highly project-dependent,governed by such factors as:• The number of data sources to merge;• The number of consuming systems to modify;• The complexity of access patterns querying the singleview. Benefits:. Present-time view of specific data. Users are utilizingthe freshest report of the data, rather than expecting for updates to propagatefrom the root systems to the single view.• Controlled application complexity. Read and writeoperations no longer require to stay segregated between different systems.
Ofcourse, it is necessary to then execute a change data acquisition process thataccelerates writes against the single view back to the source databases.However, in a well-crafted system, the mechanism need only be performed oncefor all applications, rather than read/write segregation copied in theapplication estate.• Enhanced application readiness. With conventionalrelational databases operating the source systems, it can get weeks or monthsworth of developer and DBA effort to update schemas to establish newapplication functionality. fealti’s adaptable data model with a dynamic schemasecures the addition of new fields a runtime operation, allowing theorganizations to evolve applications more swiftly. With all associated data for our business object merged intoa single view, it is possible to run advanced analytics against it.
Forexample, we can kick-start to analyze customer or client behavior to betterrecognize cross-sell and upsell possibilities, or danger of churn or fraud.Analytics plus machine learning must be able to operate across vast swathes ofdata collected in the single view. Common data warehouse technologies areinefficient to economically store and treat these data amounts at scale.Hadoop-based platforms are incapable to serve the models generated from thisanalysis or conduct ad-hoc investigative queries with the lower latencydemanded by actual-time operational systems. ENTERPRISE are using techniques like :Customer 360-degree viewRevenue WaterfallBusiness metric tracking and understanding the whyProduct A/B testing ===========================FINANCE:Risk AggregationBig data techniques can be used to gather and process riskdata in order to 1) satisfy risk reporting requirements, 2) measure financialperformance against risk tolerance, and 3) slice and dice financial reports.The Fealti Converged Data Platform package benefits risk assessment executivesas they can produce an on-demand historical analysis of risk data as well asgain real-time alerts when limitations are exceeded. New Products and Services for Consumer Credit Card HoldersMaking new products and services open to consumercardholders is a continuing action for banks.
Enhanced marketing crusades andads by effective targeting are needed in order to deliver services to customersand improve revenue for banks. The Fealti’s Converged Data Platform ispracticed to provide new products plus services to consumers in actual time ata leading credit card organization. Advanced machine learning includingstatistical techniques are exercised over data that is collected in a highlyavailable Hadoop cluster.
it gives the credit card company the capacity to usemachine learning techniques for varied purposes, including fraud detection andguidance. Next Best Offer – Banks can use predictive analytics on acombination of data to create a series of targeted offers for customers, andmake these offers available in real time at the next point of customerinteraction. HEALTHCARE:Individual e-folio of each patient record toward healthmanagement and planningThe healthcare enterprise remains to be under scrutiny andstress to reduce costs while advancing the quality of care. While theenterprise is growing more data-driven, the data landscape stretches to grow,getting it more complex for healthcare organizations to drive insight and goadvanced.Unorganized data estimates for 80% of the data thathealthcare businesses rely on and that data is increasing exponentially.Gaining access to this unregulated data – ranging from data produced by medicaldevices, practitioner notes, laboratory results, and imaging records toclinical data, genomics data, and sentiment data is precious for determiningthe precise treatment plans for enhancing patient care, and acceleratinginnovation. With easy access to all data sources to provide a singleview of the patient, healthcare organizations can:• Identify at-riskindividuals for health care conditions, such as congestive heart failure ordiabetes, and recommend proven treatment plans• Monitor patientsin real time and alert care providers the moment there is a change in apatient’s condition• Implementprovider scorecards to drive improvements and ensure consistent patient care• Reduce fraud andabuse with strict policies and procedures for safeguarding healthcare data. Patient demographic payment patterns for insurance riskGenetic and lifestyle data to forecast patient health risksHospital cost modeling Manufacturing : With an increasing competition and ever more demandingcustomers, manufacturing is never easy.
While factory level automation has significantly improvedall areas of processing for manufacturing companies, it has also created astaggering amount of data. Though the entity ismost often a customer, the benefits of a single view in enhancing businessvisibility and operational intelligence go far beyond understanding customers.A single view can apply equally to other business contexts, such as products,supply chains, Manufacturing, industrial machinery, Aviation, R&D, weather forecasting,local communities, cities, financial asset classes, and many more.For those interested in comparing various analyst views ofthe fast-growing pubic cloud market, still at its early growth stages in 2017,can refer to the Forbes ‘Roundup of cloud computing forecasts(https://www.forbes.com/sites/louiscolumbus/2016/03/13/roundup-of-cloud-computing-forecasts-and-market-estimates-2016/#31f4d1a2187f). The key takeaway for the purpose of our current conversationis that SaaS in 2016 already jumped to double-digit share of the WW IT spendand by 2020 will be nearing adulthood with a high teen share of the total WWSoftware spend (??).
These all attacks can be thwarted by our next gen armoury,With all fealti’s agile and robust techniques we collect, transforms,Visualize, Analyse and provide valuable insight into data which helps tooptimize performance, lead time, product quality, and lower production cost.Obtain the greatest productivity amidst minimum investment