Pdf Gartner Reprint Data Engineering Is Critical To Driving Data And Analytics SuccessBy Flipamilsen In and pdf 29.11.2020 at 19:25 3 min read
File Name: gartner reprint data engineering is critical to driving data and analytics success.zip
But the big data industry has significant inertia moving into
- The Need For Advanced Data Analytics in Semiconductor Manufacturing: Embrace the data or die
- Design a Data and Analytics Strategy
- Big Data Industry Predictions for 2021
Traditionally, banks targeted older customers for wealth management services, assuming that this age group would be the most interested. Augmented analytics is just one of the top 10 technologies Gartner has identified with the potential to address these and other major data and analytics challenges in the next three to five years. Digital transformation has put data at the center of every organization.
But the scale, automation, and trust required can only be achieved with artificial intelligence and machine learning, which in turn depend on best-of-breed metadata management. Download your complimentary copy of the report to see for yourself why Gartner has named Informatica a Leader again. Overall it has saved our associates an incredible amount of data research time. This product saves our company millions of dollars that would otherwise be being sunk into research time.
The Need For Advanced Data Analytics in Semiconductor Manufacturing: Embrace the data or die
The explosion in these disruptive technologies has created a surge in demand for advanced semiconductors. The Wall Street Journal recently noted:. In , there were 3. Analysts estimate revenue from chips will double, if not triple, in the next decade. They are also pure indicators of another round of explosive data growth. The manufacturing of these RF devices that will drive the technology of tomorrow is extremely complicated, generated from an abundance of complex wafer processes and designed from a vast array of components creating full systems on a chip SOC.
Consequently, the amount of data generated from manufacturers has grown exponentially — from device design through final package test. The data is structured, unstructured, complex, and requires computational mathematics, conversions and alignment to the business; it is not uncommon to see over one billion data points a week just from production test equipment. Many fabs see the management of this data as their greatest burden when in reality it could be their greatest asset.
Their productivity could improve dramatically through appropriately leveraging the data. To begin with, a sophisticated data initiative requires the appropriate level of support and corporate structure.
The preponderance of fabs today fail to fully leverage their data capabilities because of their corporate structure. Typically, the data analytics' function reports through information technology IT or within manufacturing, presenting two problems.
First, data analysis and technology are fundamentally different functions. IT managers often lack an understanding of big data analytics, and therefore fail to fully utilize the opportunities data presents. Second, and most critically, data analytics risks being siloed. Having data engineers report through IT limits their opportunities to work cross-functionally.
Sophisticated data analysis will benefit and build coordination across every functional group within a fab. A successful advanced analytical initiative requires a strategic corporate vision that needs deliberate support and prioritization from the top down, and an autonomous, stand-alone corporate business group dedicated solely to data.
Corporate executives must enable this group to incorporate and synchronize core business systems, design centers, wafer fabrication locations and production manufacturing factories. This effort will create a company-wide system that promotes cross-functional collaboration through seamless digital communication, production synergy and business intelligence. The question then becomes: who belongs on this team, and how should it be structured?
Big data analytics tools are extremely complex and programming-intensive. Perspective analytic design, machine learning, automation and artificial intelligence require a variety of skills. It is essential that this endeavor be managed by those with the proper core competencies capable of fully supporting the design development demands of the future.
An ideal Data Analytics Group is illustrated in Figure 1. The primary function of the Data Analytics Group is identifying and organizing the data being generated by the manufacturer; then creating a framework through which the flow of data can be structured and managed. The first two stacks support wafer fabrication through manufacturing and contain layers of raw data generated from test, process and capital equipment.
Data stack three contains business execution properties that house work orders, lot numbers, product identifiers, processes and workflows. Data stack four supports external data sources. The Data Analytics Group works with each business unit to identify the disparate sources of data and establish what is most important to the company.
Once the data silos and processes are identified, each is thoroughly examined and documented for ownership, structure, data content, formats and existence of business enterprise data.
The importance of enterprise metadata is crucial for successful analytics as it provides alignment between the business processes, and the data collections. Data relationships formed are used in designing connected layers that will provide insight for yield improvement, productivity, equipment optimization, performance and actionable decision making. The product of this initiative is cross-functional data alignment. Figure 3 demonstrates the data alignment requirements across business functions.
The alignment defines the exact data relationships tied to business operations, manufacturing processes, internal rules, databases, algorithms or product specific applications. The alignment provides full genealogy to the unstructured data and provides levels of manufacturing intelligence for the analytics team. The data silos contain raw data that is often in multiple formats. These files will need to be transformed and combined for analytical intent.
Software tools must be designed to provide real time extraction, translation and loading ETL of raw data files into scientific information. Database relationships are derived with the required business enterprise extracts. These associations provide connectivity and genealogy to fab processes, work orders, production and external sources. Once the design framework is reviewed and communicated, the initial design of analytics can begin, providing insight to operational efficiencies, continuous yield improvements, failure analysis, process optimization and general statistics.
Advanced analytics is a continuous process that will change with business developments, but a sound architecture provides purposeful, accurate and timely data for the entire corporation. Once data alignment framework is established, analytics can be used to create a digital die definition footprint that can be used in a multitude of processes to streamline manufacturing. Precise die definition for each wafer design, as shown in Figure 4 , is the distinctive physical footprint necessary for visual analytics, internal application design, and integration with 3rd party software.
These digital definition files are easily translated and converted. The digital files are then to be used throughout fab and fabless manufacturing operations for known good die KGD strategies, controlling die movements on capital, machine optimization schemes, upstream manufacturing and cognitive analytics.
The wafer map definition originates at design layout and is extracted through GDS files. Translation routines are used to convert this information into digital wafer maps, containing specific die definition, test structures and process control monitors. The maps can also be used to generate various machine files.
Throughout the manufacturing process, precise die definition is recorded in all data collections. Production can leverage the digital die definition to drive each of the following sequential steps in semiconductor manufacturing:. In wafer probe operations, digital wafer maps are manipulated creating specific probing profiles to optimize capital. This capability provides sample probe patterns used for high yielding components, identifies non-probe edge exclusions zones and can be used for creating post-probe maps that allow for the re-test of only the failing die.
These methods can provide capital efficiencies of up to Die Inspection. At the die inspection process, digital map results from wafer probing are converted into machine inspection files.
These wafer-specific files control the machine movements, examining only those with passing wafer probe results for visual defects. In most large-scale semiconductor manufacturers today, these efficiencies would equate to millions of dollars per year in savings.
Consideration should be given to the benefits of processing data results at a central location off line from fabrication and manufacturing tools. In this off-line model, software and services allow wafers to be screened to multiple limit sets, to calculate initial statistics, to automate wafer dispositioning processes and to create upstream machine files for inspection and die attach.
A major cost savings with this model is its ability to re-screen wafers without the need for retest. Wafers in inventory or in need of disposition can be released to production with new die attach map files from re-screened product.
These packages often support integration with other databases and contain a number of visual analysis tools for the end user. A sample of the output is provided in Figure 5. Web service portal designs, as shown in Figure 6 , allow for optimization of manufacturing tools, delivering real-time visual analytics on interactive dash boards of manufacturing performance indicators, KPI metrics, equipment efficiencies OEE and applications for full data genealogy extractions.
Today, best-in-class manufacturers leverage advanced data analytics in their production process. Manufacturers hoping to stay competitive must do the same. Product-market lead time has increased proportionally with each node of functionality.
As circuit designs become increasingly complicated, so too does the design verification process, with an enormous volume of data, necessary for predictive analytics, device analysis and machine learning algorithms.
Design engineering labs filled with expensive RF testing equipment are continuously pumping out massive files used in validating design performance across the RF communication spectrums. When one also considers the thousands of mathematical modelling computations, design simulations, reliability requirements, characterization, system software and final test needs, it is surprising that engineering staffs can produce devices in a month timeframe. To address these design demands, manufacturers must begin to invest heavily in engineering talent.
Today, their efforts are far from adequate. Most of this device analysis today is still seen as a function within design engineering. This needs a fundamental change in the semiconductor industry, where engineers are unjustifiably scarce.
In the U. A recent study conducted by the Manpower Group concluded:. Competition for top engineering talent continues to challenge employers, requiring them to re-examine attraction and retention strategies. As modeled in Figure 7 , the deep technical skill sets of software engineers and data scientists align perfectly with device design engineering teams.
Experts in data science and software engineering can collaborate to advance design verifications methods by providing rapid design insight through the deployment of device algorithms, predictive analytics, and machine-learning tools.
This insight will provide engineering staff with a deeper understanding of their designs by providing them with device-specific analytics in overall performance, reliability, and fault tolerance.
Beyond adding value by improving the immediate design verification methods, the algorithms built will develop their own substantial intrinsic value, and will prove to be invaluable assets to the entire design operation. Internal IP software developments from this partnership can form significant competitive advantages. Below, several examples of where manufacturers can benefit is provided. Advanced prototyping methodologies are one example demonstrating the value of internally developed manufacturing IP.
Advanced prototyping involves deep die parametric searches, surveying wafers to locate specific devices used for design builds. In Figure 8 , these algorithms search through wafer lots locating actual die as determined from model simulations and used for prototype builds testing design centering, corner builds and device stack-up tolerances.
The specific tools crafted for each organization support world-class manufacturing standards with methodologies targeting key performance metrics, manufacturing optimization and end-to-end line yields providing complete corporate visibility from a suite of scientific tools.
In manufacturing, data science analytics provide cost-saving initiatives through the distribution of real-time corporate intelligence used for proactive process improvements, expediting yield analysis, insight to overall equipment efficiencies OEE , active work-in-process WIP management, return on invested capital ROIC , resource management, and prioritizing quality improvement efforts.
This IP has compound benefits from maximizing engineering resources, reduced time to market with custom analytical tools that nurture an environment of engineering discovery, to a new team of data scientists who understand their needs and speak their language.
As demand for device functionality increases so does product development complexity, productivity costs, resource allocation and time-to-market.
These are all best addressed with big data solutions. The need for robust data science technology organizations in the semiconductor industry cannot be overstated. This is no longer a luxury; it is a necessity. Those investing in the science of data technology may just determine what companies successfully advance in the industry. By Mike Geen, Filtronic, U.
Design a Data and Analytics Strategy
We live in an increasingly data-driven society, in which information is becoming as much of a currency as money. Many consumers use free services from internet giants like Google, Facebook, Amazon, Microsoft and Apple, for example, and in return allow these corporations to track and monetise their online behaviour. One of the biggest questions of the day is the openness of such transactions, and the level of control that individuals have over the fate of the personal information they -- sometimes unwittingly -- divulge to organisations with which they interact online. Recent votes on both sides of the Atlantic have highlighted the capacity for data-savvy organisations to hoover up and profile large amounts of user data -- including demographics, consumer behaviour and internet activity -- in order to micro-target adverts, news stories and services in support of particular goals or causes. Clearly, the data floodgates are now opening for businesses of all sizes and descriptions, bringing myriad opportunities for timely analysis in pursuit of competitive advantage. Although the focus is currently slanted towards customer behaviour, data is available at multiple points in the product or service supply chain, and comes in many forms -- traditional structured , ad hoc unstructured , real time, and IoT- or M2M-generated, to name but a few.
The explosion in these disruptive technologies has created a surge in demand for advanced semiconductors. The Wall Street Journal recently noted:. In , there were 3. Analysts estimate revenue from chips will double, if not triple, in the next decade. They are also pure indicators of another round of explosive data growth.
Enterprise decision-makers look up to Gartner for its recommendations on enterprise software stack. The magic quadrant report is one of the most credible, genuine, and authoritative research from Gartner. Since it influences the buying decision of enterprises, vendors strive to get a place in the report. Gartner recently published its magic quadrant report on data science and machine learning DSML platforms. Gartner attempted to stack rank the vendors based on a well-defined criterion. Refer to the inclusion and exclusion criterion for details on the parameters considered by Gartner.
Big Data Industry Predictions for 2021
In , more than academic Big Data related publications could be counted Chen et al. Big Data provokes excitement across various fields such as science, governments, and industries like media and telecommunications, health care engineering, or finance where organizations are facing a massive quantity of data and new technologies to store, process, and analyze those data. Despite the cherished expectations and hopes, the question is why we face such excitement around Big Data which at first view rather seems to be a fashionable hype than a revolutionary concept. Is Big Data really something new or is it just new wine in old bottles seeing that, e. Taking the traditional financial service industry, which currently cherishes huge expectations in Big Data, as an example, the collection of massive amounts of data via multiple channels for a long time was part of the business model to customize prices, product offers, or to calculate credit ratings.
Email: solutions altexsoft. Dining at a fancy restaurant, you want to spend some quality time, enjoying tasty food and drinks. When choosing the latter, chances are you will prefer a glass of good wine, the older the better. For that matter, we all know old wine and old friends are the best.
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1. What is a legacy system?
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