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Data Blog

Lower Your Cost of Research

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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Little Firms Big Decisions

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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Trade Statistics and Blockchain

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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The Internet of Things

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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Data Blog

Law Firms and Data Science

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.

Organizational Psychology

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.

Data Blog

Research, The New Profit Center

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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Smart Government and the Data Revolution

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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Data and Output, The Second Wave

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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