Outsourcing data analytics and research has never been easier.

Author: Datafirm

March 21, 2016

Data science improves organizational function, but it isn't always cheap. Scientific data analysis enables organizations to develop more internally efficient business systems. They can more easily reach potential stakeholders and engage them. Data informed organizations can prepare forward, more accurately refine and refocus strategies, and more effectively manage risks. The problem is data science itself is an investment heavy human resource, and those costs are passed on to the employer. Trainees need a strong background in probability and statistics, and expertise in a speciality subject, like political science, medicine, engineering, or economics. Finally, to round it off, data science as a practice is a computer science intensive activity. Data scientist need proficiency and expertise in complex computer applications to get the job done.

The path is rigorous and time intensive. The result, in a global information boom with rapidly growing demand for skilled data analysts, is a shortage of deep analytical talent, and rising costs of training and retaining analytical departments. Small organizations are not typically positioned to hire or train data scientists and supporting analytical teams. Public organizations lose talent quickly to the private sector. To add to the investment challenge, new technologies, software, and data formats are developing rapidly. Training and retaining in-house people to acquire expertise in the variety of applications and languages now on the market, doesn't ensure those applications won't become obsolete in the near future. The risk is that investment in analytical resources doesn't come with any reasonable assurance long-term demands will be met. Fortunately, there are options, and organizations are starting to use them.

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High costs and high risks are prompting more and more organizations to outsource data management, data science, and analytics to a growing number of data start-ups. The market is small, compared to the accounting or law markets, but nonetheless it's large enough to sustain an already growing number of stand-alone data-focused businesses. These start-ups accumulate expertise in analytics and data science, and distribute it across a wide number of organizations and sectors. Firms taking advantage of the stand-alone data science market more efficiently meet project budgets and timelines, and limit investment risk.

Datafirm offers small and medium sized organizations and businesses the opportunity to test data science services on a pay-as-you-go basis without committing resources to training and acquiring expert staff. Organizations and departments can mitigate fluctuations in project demand to better meet deadlines. Outsourcing research and publication preparation can be cost-effective for small organizations and those facing tight-deadlines or resource shortages.

Author: Datafirm.

March 21, 2016

Big data means billions if not trillions of observations recorded daily. Sophisticated data infrastructures and advanced algorithms reveal new insights into our fast changing world, but only for those massive organizations with the transaction frequency and the know how to extract them, right?

The question is one of relevance. While big data benefits for large organizations are readily tangible--like in the case where retailers want to know when customers are more likely to purchase which products, or what optimal prices will move discounted products in a set time frame--smaller organizations sometimes just don't see how the data revolution is important to them. They might not know they have the data, and in some cases, start-ups for example, they might not. They are also more often focused extensively on the sales pipeline and do not prioritize data analytics. A focus on the sales pipeline is critical, but in many cases a failure to leverage the knowledge in data can render that focus futile. The irony is that both big data and smart data might be more important to the small organization than the large one.

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Data science is decision science with an edge, more data. In small organizations, decision outcomes are relatively larger, and impact on both the upside and the downside is greater. Take, for example, the decision of where to locate a business unit. Different locations will have consumers with different preferences for different products. Future economic conditions will impact real-estate values, rents, and product demand. Demographic data might determine if external economies of scale can optimally support human resource requirements. A large organization might more easily commit without data analysis (not recommended), if things don't work out, they can shut down the unit and absorb the loss. In a smaller organization, the economies of scale are also smaller, and there might not be enough financial space to absorb the loss.

And small organizations do have data. On-line traffic, inventories, sales, operations, fixed and variable inputs, purchasing transactions, are all data, not necessarily the big kind, but definitely the smart kind. Production, inventories, administrative data are stored in internal organizational management systems. Financial transactions data are processed in our accounting software. Customer data is almost always collected and processed in some form, through an Internet portal or customer feedback surveys, for example. Documents, like proposals and estimations, that impact forward growth of new products, expansions, and strategies, also rely on data. If data isn't being collected, it should be.

In addition, for those start-ups that don't have the historical data to inform product launches, pricing points, and location decisions, many of the insights big data players are discovering are open source, or become so rapidly. External data sources can be mined to ensure business plans adequately minimize opportunity costs.

The fact is small organizations need data analysis too, and they need not rely on training internal staff to use clumsy and fleeting over-the-counter business solutions to mine it. A new sector of data-focused organizations, in the business of aggregating and analysing data, have begun to inform organizational decisions across disperate sectors and regions with sophisticated cutting edge data science breakthroughs. Stand-alone data science services are now available without the cost and risks found in typical big data investments.

Author: Datafirm

March 21, 2016

National and international level statistics that report macroeconomic indicators, like nominal and real inflation, international transaction flows, and trade statistics, are relied on by market participants, policy makers and stakeholders as inputs to critical decisions. More often than not, macroeconomic statistics are taken as reliable, after-all, those are our official statistics, right?. But just how reliable are they? Unfortunately, the answer is that aligning statistics with underlying realities is a challenging task, and mainstream macroeconomic statistics should, even in the best of cases, be taken with a grain of salt.

Price statistics, for example, are based as everyone knows on baskets of goods that don't always adequately reflect prices. Moreover, they are compiled using sources that themselves may suffer biases, or worse over time may have fallen under the auspice of parties with an interest, however unconscious, in the magnitudes of eventually published numbers, which is a big concern for market participants gauging economic activity in foreign economies publishing 'suspect' statistics.

All this could change rapidly as a late-stage technology revolution innovation that promises to be influential is more and more commercially developed.

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Blockchain, the concept centered around a cryptographic ledger that permanently stores transactional data, has reached a phase where organizations around the world are diverting resources toward its experimentation and development. What started with Bitcoin, still the only live commercial Blockchain technology, has transformed into an entire sector. There is now Blockstream, a group enabling companies to create Bitcoin compatible protocols to leverage the existing Bitcoin infrastructure in their own products; Ethereum, a blockchain technology that facilitates its cryptographic operations with a functional programming language; the IBM backed Hyperledger, an open source Linux associated project enabling ledger privacy through permissioning; and, among others, Microsoft and R3, a group initiative combining the power of Microsoft's cloud technologies with a consortium of financial institutions to test and develop blockchain as a facilitator of 'clearing-house free' transactions.

So how does Blockchain work? The Wallstreet Journal's CIO report gives a concise explanation by citing Guardia, a provider of blockchain-based products:

'assume an organization has 10 transactions per second. Each of those transactions receives its own digital signature. Using a tree structure, those signatures are combined and given a single digital fingerprint--a unique representation of those transactions at a specific time. That fingerprint is sent up the tree to the next layer of infrastructure, such as a service provider or telecom company. This process happens for every organization in the network until there is a single digital fingerprint that encompasses all the transactions as they existed during that particular second. Once validated, that fingerprint is stored in a blockchain that all the participants can see. A copy of that ledger is also sent back to each organization to store locally. Those signatures can be continuously verified against what is in the blockchain, giving companies a way to monitor the state and integrity of a particular asset or transaction.'

Because the distributed network of participants engage through a common protocol and consensus mechanism to enable permanent historically verifiable transactions that don't require matching through third party clearing house vendors, they are resilient, in that information is not stored in one single place, and functional without third party clearing houses.

Another exciting aspect of blockchain comes through the expectation that developmental ecosystem dynamics will push vendors into new sectors. While BITCOIN has the Stackelberg advantage, which has given it lengthy peer review, stress-testing, and protocol maturity, other organizations are bound to make breakthroughs that promise to shift the global foundations of transactional economics. New market ecosystems typically consolidate as entities in specific niches reach tipping points. This pushes developing players to apply their business models and technologies in new fields.

The market for international macroeconomic transaction recording, and the statistics that are derived from them, stand to gain a lot if blockchain technologies push into international trade in goods and services. One can readily envision, though government support might be a long time coming, a crypto ledger at the bank for international settlements, for example, that is directly linked with blockchain to central and regional banks around the world. Markets with less definitive sizes, like the eurodollar market, could be more distinguishable when blockchain technologies bring international financial flow data into the publicly accessible sphere. Inflation, a mysteriously vague phenomenon, as far as accuracy is concerned, though it is regularly estimated, could become a much more exact indicator.

Blockchain technologies are one way the data revolution promises to impact statistics around the world. Such a change itself would create more market efficiency, enable governments to plan more carefully for international flows of goods and services, and reduce global long-tail economic volatility.

Author: Datafirm

March 21, 2016

The Internet of things (IOT) is a reference to computerization in devices and machines we use in everyday life. Smart homes and the smart appliances in them are examples of things that are more and more frequently connected to the Internet and programmable with computers. Online appliances transmit sensory information upstream to computer processors that make decisions, like adjusting the heat, turning on or off a hot water tank, switching up power sources, or charging electronic devices, like cars. Online computerized things create a two way process between information collection and analysis and controlled action. This process is consistent across sectors and the IOT innovations in them. Mckinsey Global Institute has categorized some distinct types of IOT applications and we comment on some of them here.

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1) Tracking applications

Tracking applications send sensory data upstream where it can be analysed and fed into the decision making processes. For example, traffic data returned through camera and road sensors can feed information into signalling algorithms so traffic flows can be managed in real-time. The same sources can inform emergency crews where stoppages are more likely to occur so they can be minimized.

Tracking applications are already being applied, in a wide variety of cases. There is now farming equipment that collects weather information and automates irrigation processes. In Japan, there are now marketing models that enable real time pricing and product offerings, for example, some billboards with camera sensors try to match their observers characteristics with opportune advertisements. Crime reduction, health care, civil engineering, exploration, self-maintaining machinery, production processes, inventory tracking, international trade, all these IOT categories and more are set to realize efficiency gains from the tracking aspect of IOT.

2) Analytics

New tracking and sensory IOT products need to be met with increased analytical demand. Upstream sensory information needs to be run through the data pipeline so that big data can become smart data, where predicting variables reflect minimal omitted variable bias, and are themselves correlated as weakly as possible. More data will need to be maintained, translated, and prepared. More effective analytics will be run on the data to ultimately better inform real-time and forward looking decisions. The coupling of information feedback with analytics creates an opportunity for business growth that will lead to economic growth, and hopefully greater prosperity for all global citizens.

3) Decision Making

Data analytics enable decision making. In the IOT market, analysis leads to better long-term business decisions, like those that include investment, design strategies, and business models; but, IOT analytics are known also for short-run decisions, sometimes as short as instantaneous. For example, automated vehicles collect and process information to make instantaneous decisions. Manufacturing process optimization also follows the pattern of information collection, analysis to make fast decisions. In forestry and mining processes, for example, real-time temperature and pressure changes can demand changes in mixtures of chemical applications. And then there is energy consumption. Home and office management systems, municipal and regional large scale energy projects, products with energy demand, all rely more and more on data driven automated decision making processes.

The advent of the Internet of things is going to change the way we live our lives, in fact it already is. The kinds of interactions we have with things are changing, as are the costs of those interactions. An indirect result of the IOT revolution will be found in the large demand for analytical services. IOT start-ups will benefit greatly from stand-alone analytical entities with the capacity to manage parts of their analytical load.

Click here to read up on the IOT at Mckinsey Global Institute.

Author: Datafirm

March 21, 2016

Information technologies and the data they create are changing the world rapidly in a multitude of ways. The legal profession, one of the most traditional and adverse to change, is beginning to adapt. A new breed of data savvy lawyers are entering legal circles everywhere and bringing with them an inertia from new technologies.

Firstly, the legal profession stands to gain from those data driven insights that are common across organizations, like more accurately targeted marketing, more informed case selection, more efficient internal procedures, and pricing models that minimize both losses of producer surplus, and dead-weight losses. But that is only the beginning.

Vast stores of hard copy legal archives are already being digitization and centralized. Harvard Law School's (HSL) 'free the law' project is a primary example. It promises to make publicly available the 'official print versions of all historical U.S. court decisions,' says HLS and Library Journal.

The digitization impact promises supply-side efficiencies that will bring down the cost of legal service. Moreover, digitization, at both the public and in-house level, has the potential, at least in terms of access to knowledge caches, to level the playing field. Fewer resources will be necessary to glean insights from the reams of physically archived print data.

Big data analytics are also changing the inter-industry dynamics of proprietary digital archives. A field that his been dominated by two main players, LexisNexis and Westlaw, is now being challenged by new comers, like Ravel Law, the big data company partnered with Harvard on the "Free the Law" project. Again, increased competition has the potential to bring down legal research costs. Lower legal research costs will enable firms to not only realize more profits, but also expand their case selection to included cases with higher anticipated variable costs.

Finally, law is an expert dependent field; both testifying and non-testifying experts are a critical input to legal proceedings on both sides. The same analytical value that can be applied to create efficiencies for organizations across sectors, can be accessed by legal organizations to better represent their client's interests. Legal organizations can gain from the data revolution by outsourcing big data analytics to analytical experts in the field where their disputes lie. The benefits from big data analytics are not likely to be underestimated in the legal field, especially in cases where data analytics can impact legal outcomes. And again, the market for stand-alone data-focused businesses can be a boon to organizations big and small.

Author: Datafirm

March 21, 2016

Numerous studies have examined what intangible and tangible factors might cause one organization to outperform others over a period of time. There is no question that ideas and talent are invaluable, but it is no longer any surprise that the single most important factor affecting organizational performance is organizational psychology. Here are a few examples:

Ray Dalio runs Bridgewater Associates, one of the largest hedge funds in the world. In Connecticut, he's got 1500 people, some of whom are the best and brightest brains in the financial sector, but does Ray cite his PhDs, research acumen, and process as his number one a comparative advantage? No he doesn't. Ray says his number one comparative advantage is his open and critical organizational psychology, the principles that underly his organization's rapid growth and incredible returns. Here in Canada, hockey is the national sport, it's our sport. Every year, teams from other countries around the world dispute this, and usually Canadian hockey players prove them wrong. But what makes a hockey team win? What is the difference between a goal in the overtime of the seventh game and a riot? Actually, it's no secret. Yes, teams need to bring together fast young players, big players, and skilled players, they need winning trainers and patient programs to develop deep talent, but success on the ice is about more than that. It's about attitude, synergy, and confidence. It's about organizational psychology.

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So what is organizational psychology, and why is it so important? Organizational psychology can be described as a set of principles and philosophies that transcribe into actions and behaviours within a firm's culture. Individuals and groups of individuals are constantly receiving information through their senses. The information is passed to the brain where it is processed by a cognitive program, or psychology, that then produces not only an action decision, but also a chemical response from the neurological system that changes the individuals mood. The change in mood can influence other members of the team, trigger further chemical excretions, and lead to changes in results.

These cognitive programs can be described as snippets that are passed between individuals in the organization, or between managers, leaders, and their followers. The upshot is that you can spell out success. It can be found in the ethos and pathos that goes into each project. These in turn impact the rate of change in organizational knowledge capital, employee utility, and as a consequence, the quality of results delivered to clients.

So here are a few of Datafirms organizational snippets:

1) Do it for the doing. People that are passionate about what they do get better at it. People who take pride in the quality of their work, do better work.

2) Success is about good habits. Time management, exercise, planning, organization, are all just habits. People that can manage their own habits grow faster and are more likely to achieve their objectives. Excellent habits lead to excellent results, excellent results lead to value for our clients.

3) Don't hate the next guy, learn from him. There are 7 billion people in the world, on every block there is somebody faster, somebody smarter, somebody better off, don't worry about it. Commit to excellence everyday, not relative the next guy, but relative yesterday.

4) Balance the standard with the innovative. Innovation is rarely lauded. A new approach, a different way of thinking, a creative strategy is typically laughed at and often scorned. People like people like them, if you're doing something new you may risk some criticism. But everybody knows innovative organizations create market share. Innovative procedures have to be planned for. Creativity needs to be fostered and supported. At the same time, tried and true is successful for a reason. Mans success as a species is due largely to knowledge spillovers. There is a lot to be said for engaging the standard procedures.

5) Constructively open criticism can lead to organizational growth. This practice is a cornerstone of Bridgewater's success. If you have something to say about someone say it to their face. The practice ensures individuals are getting the feedback they need, and critics have to develop enough tact to deliver criticisms without sinking the boat. Politic is an inherent property of human organization, but it is also a costly one. How much of your organization's energy is spent establishing and maintaining hierarchy? How many steps backward have to be taken because someone is sabotaging the success of the organization for their own gain? Constructively open criticism is one way to streamline organizational politic. Good changes have a better chance of getting traction, if unwelcome disturbances are going to happen, they will happen more quickly.

Keep in mind, Datafirm is in the business of data analytics and research, not organizational psychology. That said, we want to create an environment where our people can perform at their best. Great performance yields great results, great results mean better value for your organization. Our goal is to enable your organizations to deliver more informed services and products. We use data science, decision science, analytics, and research to do that. Our organizational psychology enables us to deliver you results that grow forward in value.

Author: Datafirm

March 21, 2016

Enhanced financial regulation typically comes with intended and unintended consequences. Directive 2004/39/EC, the Market for Financial Instruments Directive (MiFID), for example, successfully increased competition among financial instrument trading organizations in Europe. This brought down consumer prices and narrowed margins. It also sent market participants looking to move large blocks of shares into dark pools, where trade data are not made public.

There will be intended and unintended consequences from MiFID's successor, MiFID2, which is anticipated to be in effect by January 2018. Laura Noonan, of the Financial Times (FT), identifies one anticipated effect when she writes here, "MiFID2 will reshape the way analysts report on companies and how the research can be priced and circulated to investors."

She's referring to the expectation that banks will no longer subsidize research departments, and will instead look to charge consumers of financial products for research on a stand-alone basis. That kind of potential change to legislation has banks in Europe experimenting with data to determine what kind of research consumers are willing to pay for, and how much they are willing to pay.

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The upcoming changes are also a concern to research departments, some of whom believe an already shrinking number of in-house analysts will be reduced further as banks look for ways to turn research cost centers into research profit centers. While Noonan suggests the changes will trigger an impending flight to quality, there may also be other effects. For example, there may be further movements into the financial research market by non-dealer organizations, like media outlets, and the continued emergence of growth among stand-alone research providers.

The latter could occur because leaner research departments would be less flexible when research demand fluctuates with market volatility, and more likely to outsource research to external research providers. Those providers would see increases in intermittent client numbers and growth to bottom lines.

While MiFID2 will undoubtedly refine pricing and quantity in financial research markets, the anticipation of such a change is in itself an argument for outsourcing research. When there is a cost center, there can be too much supply and not enough demand. Down-sizing the cost center and outsourcing to meet intermittent demand can turn a cost-center into a profit center.

Click here to read up on MiFID2.

Author: Datafirm

March 21, 2016

The data revolution is impacting government in a number of ways. The foremost is known as 'smart government', which is a reference to the way governments manage the data they have and the way they make that management and the services that go with it available to their clients (constituents in a democracy). The second impact, also intelligent, is known as the 'smart city.'

Smart government is a concept championed by the Estonian government (our example), which won the United Nations Public Service Award, in the category of Promoting Whole of Government Approaches in the Information Age, for their online business registry.

Smart cities are a reference to the ways cities are being made smarter through the use of data science programs and technologies in city managed fields like urban planning, civil engineering, and energy use.

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The miracle of Estonia's online government portal is found in the application of efficient relational database theory that limits the duplicate storage of data. Data is not actually stored in one place, but because there is no overlap, and each database is connected by the users identity, which serves as a primary key, users view all interactions with public services through one portal. The orthogonal system means health records, traffic violations, employment data, tax records, and all municipal and national level program interactions, are available to the user quickly and efficiently. Moreover, citizens are entitled to view which government officials have viewed their data, and how frequently. The idea is that government is empowering citizens by giving them control over their own data. At the same time, smart government reduces overhead and clumsy administrative burden. Estonian citizens, for example, can register a business and within 18 minutes start trade.

Ultimately, the data revolution offers government the opportunity to streamline its operations and more efficiently engage with citizens, but it also offers information that can be used to engage technology, like the Internet of things, in a way that promises citizenry more efficient and less expensive living, through the transition to smart cities.

The smart city is entity created from the union of data science and urban planning. Smart cities have citizens that can get from point a to b more efficiently. They have more intelligent crime prevention and therefore lower crime rates. Their power generation and distribution are more efficient. They have better city water and sewage systems.

While the New Jersey Institute of Technology suggests the smart city technology industry could turn into a 27 billion dollar market by 2023, the benefits to citizens could be much higher. The OECD estimates traffic congestion costs the city of Toronto 3.3 billion CAD in lost productivity annually. According to Reuters, US citizens spend on average 42 hours a year stuck in traffic. Smart city technologies have the promise of not only improving financial measures of standard of living, but also improving the utility citizens get from the cities they live in.

But how do cities get smarter? The answer starts with data initiatives. More and more cities are implementing programs to plan for data collection, processing, and data driven research. Sensors that relay information on water pressure, peak water demand, traffic congestion, power usage, and more, are being installed, replaced and in some cases designed. Municipal governments are outsourcing more data science to ensure collected data is rigorously analyzed. The shift is really about directing funds toward research and planning so that the right investments can be made at the right times.

Cities are also upgrading their own statistical analysis at the municipal level. This can be done by envisioning the city itself as a closed system, and learning to track economic flow data across and within that system. For example, at the national level, input/output tables are used as a means of drawing conclusions about inflows and outflows of goods and services. New methods have been derived for drawing conclusions at the municipal level from input output data. Smart city data initiatives have a lot to gain from those new methods.

For example, it is possible to extrapolate from larger data sets meaningful inference about regional statistics. More comprehensive and updated regional statistics, like those derived from updated business registries, enable more accurate estimates of municipal output by sector. More accurate municipal output estimates can inform policy makers who can then engage citizens so that real changes can be made.

Better survey design and more frequent surveying can allow local governments to estimate transaction flows across municipal boundaries, which enable preemptive measures to guard against long tail economic shocks, like real-estate market movements away from equilibrium.

Models that predict and forecast indicators at the regional level using data input from the national level, are enabling municipal governments to pursue smarter policies, and attract the kind of investment that can stimulate local economies by creating jobs, and ultimately lift standards of living.

The data revolution is enabling governments to engage with citizenry more efficiently by upgrading the methods and technology they use to manage their data stores. It is also giving them the information they need to pursue smart city initiatives that promise to guide policy more accurately and lift standard of living at the city level. Whatever else may be said of government, there isn't any question they are getting smarter, and that intelligence can have benefits.

Read more about Estonia's smart government here.

Author: Datafirm

March 21, 2016

The internet of things promises to impact gross domestic product in a meaningful way, as well as, the demand for data science and research going forward.

And at the same time, most organizations openly suspect more gains are possible from those same rival and non-rival data sources. 67% of companies surveyed in the Netherlands agreed the potential for data gains in their organization were great. While larger surveyed organizations were typically satisfied with their data knowledge resources, smaller and medium-sized professional organizations were decidedly less satisfied.

Since this article was written (it was updated in 2020), even with the corona virus pandemic, production possibility frontiers have continued to grow and the Okun Gap has increased due to unrecognized gains from newly generated data assets.

Organizations from business to consumer sectors to finance and insurance have been rapidly tooling their data analysis capability to improve decision by rendering the changing digital footprint into meaningful direction. This has already begun to reduce economic distortions.

Investment in analysis has been strongest in business to consumer sectors, which have responded by fracturing the monopolistic power of big brands and enabling new organizations to bring products to market that consumers want more. While redistribution of market share is not given to be GDP enhancing, in many respects one can reason it is. For example, new firms bring new energy to market places and more are susceptible to growth in economies of scale. As generations change, product cycles develop and reduced market share for firms from previous generations can be met easily with greater gains in newer firms that are more innovative and create market share at lower costs.

In all areas data is improving GDP and, into 2021 when the world economy recovers, it will continue to do so.

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Minimization of distances over which products and components are transported will reduce costs. Logistic optimization is data driven. Weather, traffic, fuel prices, are each real world occurrences that can be modelled mathematically and stochastically. Accuracy and usefulness of outcomes improves with more data and more powerful data.

Sensor application in OEMs will continue to yield surprising efficiency gains and reduce prediction of component failure. Moreover, better tracking and monitoring of manufacturing processes will enable more intelligent understanding of optimal sensor location. Companies now better tooled with data smart equipment and savvy data analysts will discover new ways to apply data insights and generate product.

As demand for data capacity grows, low rents in developing economies will attract tech investment that will be matched with explosive growth. Huge companies, like London equity firm Actis are already investing hundreds of millions of dollars in tech infrastructure in Africa. What happens when resources are properly allocated to regions that have long been resource deficient? It's quite possible economies across the African continent in the coming decades will be home to the fastest growth the planet has ever seen.

Government transaction, licensing, commercial transaction data, internet search tracking, physical tracking data, and image data still are all still improving government efficiency and will enable governments to improve utility and decrease poverty in unheard of ways, as is happening already in countries like New Zealand.

Potential for power generation in the lower marginal cost energy sector, like the wind, wave, and solar power already improves rapidly due to data analytics. Power relies great on design, prediction, and sensory, all of which are improved.

In health care, identification of rare disease gets easier and easier as more analytics are brought to bear. Moreover, data analysis aids in discovery of cures. For example, the tragidy of the corona virus has been managed intelligently in many places due to the application of data anylitics, and a cure may be found soon.

The technology boom has generated a now not so new sector that is already contributing unprecedented gains in global growth, even in the face of the worst economic and health crisis in the century.