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ISEBSWTINT-001 ISEB Software Testing Intermediate

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ISEBSWTINT-001 exam Dumps Source : ISEB Software Testing Intermediate

Test Code : ISEBSWTINT-001
Test Name : ISEB Software Testing Intermediate
Vendor Name : ISEB
: 25 Real Questions

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ISEB ISEB Software Testing Intermediate

UniversalExams.com launches new practicing kit | killexams.com Real Questions and Pass4sure dumps

The ISEB verify administration certification is the next level of competency in utility testing after the ISEB foundation and ISEB Intermediate tiers. earning this certification makes employment candidates more constructive and marketable, as it signifies a strong ability to analyse, synthesize and consider initiatives concerning software checking out administration. These competencies are vital to the profession development of application testers, above all during this age of global competition and financial uncertainty.

like any UniversalExams.com practising packages, an approved and highly skilled ISEB route tutor authored the ‘verify administration’ direction. The path is absolutely according to the authentic ISEB examination syllabus and contains pleasing getting to know tools proven to maximise the absorption of the cloth.  This and all other popular checks practicing kits include an ironclad, a hundred per cent assure: pass, or get a full refund, no questions requested.

“UniversalExams.com became shaped in 2001 with the intention of creating QA certification reasonably-priced,” said advertising and marketing manager David L Brister. “previously, the only true approach to prepare for a software tester certification examination became to attend a dear two or three day instructor-led route. Their programs are guaranteed to deliver the same stage of coaching, at a fraction of the cost.”

The examine administration study kit includes a complete analyze guide, practice assessments, fifty six dual-sided flashcards, state of affairs question papers, examination assistance, bonus material, ninety days full working towards support in the private enviornment of the enterprise’s IT certification discussion board, and greater. additionally, the package is obtainable in numerous birth strategies – immediate down load, set of CDs, or everything supplied in a bound, full coloration booklet (with the bonus material on a CD).

“we've at all times put forth fantastic effort to present their purchasers a choice,” spoke of Brister. it is why they offer numerous delivery formats. Their business started solely as a company of audio working towards items…seeing that then, based upon customer comments, they now have brought further content and start codecs and should proceed to accomplish that in the future.”  

widespread assessments also presents product bundles that offer a brilliant cost for the funds. Product bundles encompass the ISEB basis + ISEB Intermediate practising equipment, or the 'ISEB master Bundle', which contains all 4 ISEB exam self look at kits (ISEB foundation, ISEB Intermediate, ISEB verify administration and ISEB examine analysis). UniversalExams.com is keenly concentrated on its mission to be a one-cease store for ISEB examination training practicing substances. The enterprise’s self study working towards programs present the same content material and results as normal instructor-led lessons, however at a fraction of the expense. purchasers can rest assured that all practising items are finished, most economical and warranted to supply the suggestions obligatory to flow any ISEB exam with confidence.


Maveric systems Achieves suggestions methods Examinations Board (ISEB) Accreditation | killexams.com Real Questions and Pass4sure dumps

Maveric methods, considered one of India's right three unbiased application testing businesses with a strong focal point in the banking, fiscal capabilities and coverage (BFSI) vertical, these days announced that it has been permitted through the united kingdom – based mostly, tips methods Examinations Board (ISEB) of The British computing device Society (BCS). identified by means of company properties in Europe as a pre-requisite for utility trying out professionals, the coveted ISEB accreditation affirms that Maveric has matured content material, procedures and start mechanisms for trying out competency construction.

Maveric at this element of time has a big presence within the UK and is within the manner of providing pilots within the onsite-offshore model. The advantage companions consist of prominent system integrators within the enviornment of assurance, telecom and healthcare.

said Mr. Ranga Reddy, CEO, Maveric systems, "UK is an extremely vital market in Maveric's increase approach with their operations doubling each quarter. As competitors turns into greater intense in this geography, this acclaimed accreditation gives us a distinct competencies, as we're the best Indian business operating in UK with this accreditation".

Mr. Ranga Reddy introduced, "Maveric has utterly met its fiscal projections for 2005-06 and established a sound base in the middle East and US. Projections for the subsequent 12 months are to the tune of 7 Million dollars with a powerful emphasis on offshore beginning. additional in 2006-07, they might be investing significantly in productisation of Maveric's proprietary test frameworks, in the banking vertical".

About Maveric techniques

Maveric methods Ltd. is certainly one of India's exact three independent utility-testing businesses with a robust and dedicated focus in the banking, financial services and coverage verticals considering the year 2000. over the past five years, Maveric has been pioneering the outsourcing model in application checking out via virtue of its insightful capabilities within the banking and monetary functions vertical, amazing core management group and multi-locational checking out amenities. As a leading pure-play, unbiased application trying out company, Maveric tremendously enhances the performance, usability and efficiency of IT solutions, empowering its consumers with a particular charge expertise. Maveric's miraculous client profile comprises a wide selection of leading utility product organizations, equipment integrators and financial institutions. With worker strength of 300, Maveric has a big presence in Bangalore, Chennai, Dubai, Hyderabad, London and Mumbai.

About assistance methods Examinations Board (ISEB)

ISEB skills add value to skilled careers through offering each the capability and the platform for cognizance and enhanced profession building. via ISEB, BCS supply industry-diagnosed qualifications that measure competence, capacity and performance in many areas of IS, with the intention of elevating industry requirements, advertising profession construction and featuring competitive facet for employers. until date, ISEB issued over a hundred and fifty five,000 IT skills across all disciplines of IT. The ISEB portfolio now offers a huge variety of IT qualifications in numerous disciplines

The basis certificate in software testing provided with the aid of ISEB now has dual accreditation with the ISTQB (international software checking out skills Board) and successful candidates will obtain certification with each ISEB and ISTQB awareness at the foundation degree.


Quexst Brings advice programs direction To India | killexams.com Real Questions and Pass4sure dumps

suggestions programs Examination Board (ISEB), a universally diagnosed qualification provided by way of the British computing device Society (BCS), hitherto might simplest be received by means of definitely giving the examination in the UK, can now receive sitting right right here in India. This has been made viable due to the alliance solid through Pune-based Quexst acquaintances with UK-primarily based Unicom Seminars Ltd.

talking to CXOtoday, Vandana Dandekar, joint chief executive officer of Quexst, mentioned, “ISEB is a single certificates route touching all fundamental areas of assistance programs (IS) including management, construction, carrier delivery, and quality.”

in keeping with Dandekar, corporates like Satyam have their personnel certified the entire way from UK. despite the fact, with this alliance, Quexst turns into the first enterprise to deliver the ISEB training in India. The BCS in affiliation with Unicom would supply the lessons in India.

Quexst offers exhaustive training that customarily stretches between three to five days. The examination is supervised with the aid of the computing device Society of India, while the papers are corrected with the aid of the BCS. The certification too is awarded from BCS.

when it comes to eligibility, aside from the necessary commencement, the degree of content is such that it necessitates arms-on application construction experience of two to 3 years.

charge-intelligent she mentioned it is relatively much less costly than what is offered in UK. Disclosed Dandekar, “The route payment levels from Rs 15,000 to Rs forty,000, while the examination fees may be between Rs 5000-7000.”

So, who're their target viewers?

Says Dandekar, “In view of the steep prices of the lessons, they predict corporate sponsorships for personnel who would want to take in the path. additionally, when developers are ISEB-licensed, IT groups can derive stronger mileage in terms of positioning their items or services in the international markets, above all in the European market.”

beneath the existing agreement with Unicom, Quexst will present the courses in Delhi, Calcutta, Pune and Mumbai. in the meantime in the Southern belt, they plan to present it in two facilities from the three zeroed right down to specifically, Hyderabad, Chennai, and Bangalore. Six months later, they plan to present it in cities corresponding to Nagpur, Indore considering that these are feeder cities to the IT industry.

Quexst buddies presents functions in software checking out, technique consulting and working towards functions.


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7,700 Singaporeans received inaccurate healthcare subsidies due to software error: MOH | killexams.com real questions and Pass4sure dumps

SINGAPORE: An error in the computer system administered by NCS caused about 7,700 individuals to receive inaccurate healthcare and intermediate- and long-term care subsidies, the Ministry of Health (MOH) said on Saturday (Feb 16).

The affected individuals are among those whose applications or renewals of their Community Health Assist Scheme (CHAS) cards were processed from Sep 18 to Oct 10 last year.

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The error arose in the computer system when it calculated means-test results. 

The means-test system calculates the healthcare subsidies which individuals are eligible for, based on their income information. Healthcare subsidies are means-tested so that greater financial support is extended to lower-income households.

"The means-test and subsidy tiers for all affected individuals have been corrected by Feb 16, 2019,” MOH said.

MOH said no proactive action is required on the part of the affected people at this point. The ministry added that it is working closely with healthcare service providers and scheme administrators to reach out to those affected.

Advertisement Advertisement READ: CHAS IT error: A timeline of how it unfolded

"About 6,400 individuals received higher subsidies than what they are eligible for. The total amount is estimated to be about S$2 million. MOH intends to recover from NCS the costs and expenses incurred as a result of this incident, as allowed for under their contract.

"About 1,300 individuals received lower subsidies than what they are eligible for. The total amount is estimated to be about S$400,000. This will be reimbursed to the affected individuals," MOH said. 

MOH said discrepancies in the means-test results of a few CHAS cardholders were noticed by the CHAS card processing team in MOH Holdings when it was reviewing CHAS applications and appeals.

"We received three appeals for the group affected," MOH said. 

The first case of discrepancy was detected on Sep 24, 2018 and NCS was alerted immediately, MOH said.

“The issue was initially attributed to intermittent network connection problems. Five more cases were subsequently detected between Oct 9 and Nov 2, and a more thorough investigation was initiated,” added the health ministry.

In late November, NCS traced the root cause of the discrepancies to a software version issue on a server used by the means-test system when it was migrated to another government data centre in September.

“This resulted in the means-test results being computed without the requisite income information. NCS further discovered that their deployment team had in fact fixed the software version issue earlier on Oct 10, 2018 in response to an unrelated slow performance issue.

“This stopped further cases of errors but it did not correct the means-test results that had been generated from Sep 18, 2018 to Oct 10, 2018,” MOH said.

CORRECT SUBSIDY TIERS RESTORED BY FEB 16

On Dec 5, MOH received NCS' assessment that the glitch could have affected thousands of people across multiple schemes.

Before making any public announcements, MOH said that it was important to rectify the root cause of the glitch in the system to prevent more inaccurate means-test results.

It was also important to fully establish the different groups of people and subsidies that were affected, and how they were affected, as well as limit the impact of inaccurate means-test results from affecting more schemes and individuals. 

MOH added that it was important to have a clear rectification plan that can be deployed quickly after the announcement, to reach out to the affected individuals to inform and reimburse them, where applicable. 

"If they do not do so, they could cause confusion and uncertainty, not just among the affected, but the larger group of unaffected. Scheme administrators and service providers will also face many queries for which they are unable to respond," MOH said. 

MOH said that it worked with NCS from December to establish the extent of the impact, including the correct subsidy tiers for each individual under the different services and schemes. This was to determine who could have received higher or lower subsidies than what they were eligible for.

The final assessment was completed on Jan 14 and MOH worked with grant scheme administrators and healthcare institutions to finalise the remedial action plans, including how affected individuals will be informed and reimbursed.

The correct subsidy tiers of all affected individuals were restored by Feb 16. 

Service providers and scheme administrators will now progressively inform the affected individuals and arrange for reimbursements where applicable. 

“We expect all the affected individuals to be informed by mid-March 2019,” MOH said.

MOH said that NCS has acknowledged the error and has taken further remedial action by tightening the system deployment processes.

“Additional safeguards have been put in place to prevent any recurrence of such incidents. NCS has reiterated its commitment to being held to the highest standards as a service provider.

MOH takes a serious view of the incident, and has worked with NCS on appropriate remedial measures. MOH will work with NCS on measures to prevent such errors in the future,” the ministry stated.

NCS is an information and communications technology (ICT) and communications engineering services provider. It is a subsidiary of the Singtel Group.

According to its website, NCS offers services that include consulting, technology solutions, as well as systems development and integration. 


TIPTOP Certify your way | killexams.com real questions and Pass4sure dumps

Pradipto Chakrabarty tells you why one should get certified

In a world where recruiters take mere seconds to scan through most resumes, a relevant certification inserted at the right place, can make your resume stand out from the rest.

Certifications tell your prospective employer that you are prepared for a job in an all-round manner. They also indicate to your peers that you take your job role seriously. They act as a signal of your competency in roles where basic and practical skills are required for  the evolving technology industry.  IT certifications signify that you are well-versed with practically demonstrable skills and will take incorporate emerging technological scenarios in your accounting.

  • For beginners, a core IT certification, such as CompTia's A+, that covers all the pillars of a digital organisation such as devices, networking, cybersecurity, software and operating system, irrespective of platform, lets people know that you are skilled enough to troubleshoot across technologies and is the very first step to gaining a foothold in the newage technical support space. With time, mainstream computer systems will come to include mobile devices as well. Hence, Android, iOS along with Linux may be required knowledge for helpdesks of tomorrow.
  • For workforce who have a couple of year's experience in IT, intermediate IT certifications in networking (Such as CompTIA Network+) or cyber security certifications (such as CompTIA Security +) helps prepare them for critical threats, protection against which is so important for retaining the competitive edge in today's cutthroat world of business. Cyber-security certifications in particular, arm you with the new versions of  threats and vulnerabilities in the interconnected world of business. Such certifications open your eyes to a world of possibilities with fairly specialised knowledge of complex systems and processes.
  • For seasoned professionals, certifications on emerging technology or a new aspect of an established field of practice lets your employer know that you're eager and willing to improve, are proactive and ambitious. Almost all the jobs of near future will concentrate on AI (Artificial Intelligence), IoT (Internet of Things) and Cybersecurity. Cybersecurity for instance as a career path  Pentest+ and CySA+ help you prepare for critical skills such as VAPT (Vulnerability Analysis — Penetration Testing), Cyber forensics and incident response.
  • Employers, too, are benefitted by their corporate certification training decision by providing greater job fulfillment and satisfaction. Human Resources (HR) earns points for employee engagement and aids map careers and development. However, employers need to monitor progress of trainings and some might even have to micro-manage in order to achieve the desired benefits of the module.
  • Finally, obtaining relevant certifications show your current and future employers your quality of continuously wanting to improve yourself. You must always be ready to do research before selecting the right certification that can help you get the objectives you want.
  •  After a basic computer IT course certification, experts recommend that you work in an IT organisation for at least nine months, implementing, installing, maintaining, troubleshooting and configuring simple to mid-level complex networks and computer systems before you move into more specialised fields such as cyber-security.

    Enterprise technology certifications are incomplete without knowledge of how complex servers and routers manage information, communication, transmission and encryption. Certifications are cheaper to get than full-fledged degrees, offer greater flexibility in terms of both learning content and outcomes and look good on your CV too!

    The writer is Regional Director, CompTIA India


    The Big Data Revolution Will Be Sampled: How 'Big Data' Has Come To Mean 'Small Sampled Data' | killexams.com real questions and Pass4sure dumps

    One of the great ironies of the “big data” revolution is the way in which so much of the insight they draw from these massive datasets actually comes from small samples not much larger than the datasets they have always used. A social media analysis might begin with a trillion tweets, use a keyword search to reduce that number to a hundred million tweets and then use a random sample of just 1,000 tweets to generate the final result presented to the user. As their datasets get ever larger, the algorithms and computing environments they use to analyze them have not grown accordingly, leaving their results to be less and less representative even as they have more and more data at their fingertips. What does this mean for the future of “big data?”

    Stepping back from all of the hype and hyperbole, there is considerable truth to the statement that they live in an era in which data is valued sufficiently that they believe it worth the time and expense to collect, store and analyze it at scales that significantly exceed those of the past.

    It is just as true that many of their beliefs regarding the size of the datasets they use are are absolutely wrong. In particular, many of the vanguards of the big data era that they hold up as benchmarks of just what it means to work with “big data” like Facebook and Twitter, are actually vastly smaller than they have been led to believe.

    In many ways, much of the size of the “big data” revolution exists only in their imaginations, aided by the reality distortion field of the big web companies that tout their enormous scale without actually releasing the hard numbers that might lead us to call those claims into question.

    Most troublesome of all, however, is the way in which they analyze the data they have.

    Computing power continues to increase and in today’s world of GPUs, TPUs, FPGA’s and all other manner of accelerators, they have no shortage of hardware on which to run their analyses. The problem is that despite nearly unfathomable amounts of hardware humming away in the data centers of the big cloud companies, they are still just as hardware constrained as they have always been.

    We may have vast amounts of hardware, but the datasets they wish to analyze are even larger.

    It is a truth of the modern computing era that it is far cheaper to collect and store data than it is to analyze it. In many ways this is an ironic reversal from even just a decade ago in which many large scientific computing codes would recompute intermediate results from scratch rather than store them to disk due to it being faster to compute the result again than load it from a large direct attached HPC storage fabric.

    Where CPU once outpaced disk, today it is the opposite. Storage is now so cheap that keeping multiple copies of a petabyte across multiple geographically distributed datacenters for maximum redundancy costs less than $10,000 a month in the cloud. Storing a single copy in an on-premises JBOD costs less than $25,000 worth of NAS-grade drives and prices continue to fall.

    The ratio between the size of their data and the computing power they have to process that data isn’t remarkably changed.

    My IBM PS/2 Model 55SX desktop in 1990 had a 16Mhz CPU, 2MB of RAM and a 30MB hard drive. That’s roughly one Hz of processor power for every 1.87 bytes of hard drive space.

    Fast forward almost 30 years to 2019. A typical commercial cloud VM “core” is a hyperthread of a 2.5Ghz to 3.5Ghz (turbo) physical processor core with 2GB-14GB RAM per core and effectively infinite storage via cloud storage, though local disk might be limited to 64TB or less. Using the same CPU-to-disk ratio as 1990, that 3.5Ghz turbo hyperthread would be paired to 6.6GB of disk (actually, 3.3GB of disk, taking into account the fact that a hyperthread is really only half of a physical core).

    Such a comparison isn’t quite fair given the capabilities of newer CPUs and the vastly improved speeds of modern disk systems, but it is worth pointing out just how far the ratio between CPU power and disk storage has slipped over the past three decades.

    Even when it comes to data transfer rates, little has changed in those 30 years. The disk transfer rate on the 1989 IBM PS/2 Model 55SX was rated at 7.5MBPS (around 0.94MB/s). Dividing the CPU’s 16Mhz clock speed by 0.94MB/s works out to around 17 bytes/s per Hz. Fast forward today and a typical cloud VM might max out at around 180MB/s maximum sustained read from standard persistent disk. With a turbo clock speed of 3.5Ghz hyperthread / 180MB/s that works out to around 19 bytes/s per Hz, roughly the same disk transfer rate per Hz almost 30 years later.

    Of course, there are many other factors that influence the speed with which data from disk can actually be utilized by a CPU in practice, but no matter how you slice it, in terms of the ratio between the performance of their storage systems and the performance of their CPU systems, they haven’t really undergone a transformative shift in the decades since I sat at my first desktop. Moreover, the virtualization of the modern cloud enacts a steep cost that slows it even further.

    Modern clouds address this differential through data parallelism, sharding data across multiple virtual cores. Yet, to maintain the same ratio of CPU to disk to process a petabyte with 3.5Ghz virtual cores in the modern cloud would require a cluster of 152,381 VM cores, assuming perfect linear scalability. A 10PB dataset would require 1.5 million VM cores. In reality, communications overhead and the limits of hardware scaling mean the number of cores required to achieve theoretical scaling results would be considerably higher.

    Amazingly, the CPU cost for all those cores would only be a few hundred dollars to a few thousand dollars per hour of analysis and a few thousand dollars to store the petabyte for a week (not counting the time it takes to upload or ship).

    Moreover, platforms like Google’s cloud make it trivial to scale across large datasets, with BigQuery able to table scan a full petabyte in under 3.7 minutes with one line of SQL, no programming or manual data sharding required.

    We obviously aren’t lacking for the hardware to process petabytes or even tens or hundreds of petabytes.

    In fact, BigQuery even offers fixed rate pricing starting at $40,000 per 2,000 slots that allows companies to perform unlimited queries over unlimited volumes of data for the same fixed monthly cost. A company’s data science division could query multi-petabyte datasets non-stop 24 hours a day for the same fixed cost, removing cost as a limiting factor in performing absolute population-scale analyses.

    The problem is that when it comes to big data analyses there seems to be a tremendous gulf between the companies performing population-scale analyses using tools like BigQuery that analyze the totality of their datasets and return absolute results and the rest of the “big data” world in which estimations and random samples seem to dominate.

    Estimations are particularly prevalent in spaces like social media and behavioral analysis.

    In a world in which all it takes is a single line of SQL to analyze tens of petabytes with absolute accuracy, why is estimation so popular?

    Partially the answer is their preoccupation with speed over accuracy. Why wait minutes for petascale analyses when you can wait seconds for a random sample that may or may not bear even the slightest resemblance to reality?

    In turn, their ability to tolerate immense error in their “big data” results often comes from the fact that the consequences for bad results are minimal in many “big data” domains. An analysis of the most influential Twitter users by month for a given topic over the last few years doesn’t really have a “right” answer against which an estimation can be compared. Moreover, even if the results are entirely wrong, the consequences are minimal.

    Partially the answer is that their more complex algorithms have not kept pace with the size of the datasets they work with today. Many of the analytic algorithms of greatest interest to data scientists were born in the era of small data and have yet to be modernized for the size of data they wish to apply them to today. Few mapping platforms can perform spatial clustering on billions of points, while graph layout algorithms struggle to scale beyond millions of edges.

    In short, the volume of data available to us has outpaced the ability of their algorithms to make sense of it. In the past they had low volumes of very rich data. Today they have high volumes of very poor data, meaning they have to process much more data to achieve the same results.

    When their algorithms aren’t scalable, they are left processing the same amount of data as before, but that sample is less and less representative of the whole.

    A classical SNA graph analysis of the past might involve a network with a few thousand or tens of thousands of edges, which could be visualized in its entirety even by the software of the day. Today they can easily obtain graphs in the billions of edges, but their most common graph visualization tools struggle to produce useable results in reasonable time with anything more than a few hundred thousand edges.

    That means that while a visualization of the past might have reflected the entire graph, today that graph is more likely to represent just a miniscule fraction of the full dataset. A 10,000-edge graph visualized in Gephi shows the totality of its structure. A 10 billion-edge graph sampled down to 10,000 edges represents just 0.0001% of the total graph.

    It isn’t just a question of speed. Many of their most heavily used algorithms were never designed to work with large datasets even when speed is not of concern. Few graph layout algorithms can do much to extract the structure of a dense trillion-edge graph even if given limitless computing power and as much time as they need to complete.

    In short, they are confronted with the paradox that the more data they have, the less representative their findings are due to the need for sampling.

    Platforms like BigQuery are slowly changing the algorithmic side of that equation. As they move beyond their reporting roots towards providing high level analyses like geospatial analytics, they are beginning to externalize the kind of massive algorithmic scalability that Google uses internally to bring more and more algorithms into the scalable cloud era. As these trends progress, algorithmic scalability will steadily become less of a limiting factor for common use cases.

    That leaves the mindset factor.

    We need to move beyond the idea that it is acceptable to perform the “sampling in secret” used by some social media analytics platforms in which certain displays quietly rely on sampling even while they tell their users they perform absolute counts.

    Sampling can be useful in certain circumstances. However, they need to understand precisely when and where it is used, the size of the sample and how that sample was selected. Understanding that a visualization was based on a random sample of just 1,000 out of 100 million matching tweets might give pause to how representative its results truly are. Moreover, if that “random sample” is actually based on randomly selecting 100 dates and taking the first 10 tweets from each date in chronological order, that may result in very different findings than sampling at the tweet level. Having visibility into these methodological decisions is absolutely critical.

    Perhaps the biggest issue is that they need to stop treating “big data” as a marketing gimmick.

    As companies have begun to market themselves in terms of the size of the datasets they hold and analyze, we’ve let go of the idea of actually understanding anything about the data and algorithms we’re using.

    Even the most rigorous data scientists freely accept the idea of reporting trends from Twitter without having any idea just what the full trillion-tweet archive from which they are working actually looks like. In fact, few data scientists that work with Twitter even know there have been just over one trillion tweets sent.

    Understanding your denominator used to be something that was considered sacrosanct to data science. Somehow, we’ve reached a point where they just accept reporting findings from analyses where they no longer have a denominator.

    Once they start treating “big data” analysis as a methodologically, algorithmically and statistically rigorous process based on well understood data, they recognize the need for population-scale analyses with guaranteed correctness. They turn to platforms like BigQuery and its ilk to run their analyses and focus on accuracy and completeness. They modernize the algorithms they need. They relentlessly test their results.

    Most importantly, they restore the denominator to their workflows.

    Putting this all together, it is ironic that in a world drowning in data, they have increasingly turned to sampling to render their “big data” small again. As their datasets have grown in size, they have sampled them ever more aggressively to keep the actual amount of data fed to their analyses and algorithms roughly the same. Whereas in years past their analyses might have been based on the totality of a dataset, today that same analysis might consider just one ten thousandth of one percent of that dataset. As they have ever more data, their results are becoming ever less representative.

    In short, their vaunted “big data” revolution has actually resulted in less understanding of their world through less representative data than ever before.

    In the end, they need to stop treating “big data” as a marketing slogan and bring ourselves back to the era where they actually cared about the quality of their data analyses before they undermine the public's trust in the power of data.



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