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Five Intuitive Predictions About Analytics

 

David Wagner
David Wagner, Community Editor
 

A2 is quite rightly focused on the present and the near future. But sometimes to make sure you don’t get bogged down too much in the present, you want to look ahead to get a sense of where you are going. So I decided to take some time to think of what analytics might look like at least five years out.

At first, I searched high and low for statistical trends and wanted to do my best at using analytics to predict the future of analytics. Then, part way through, I decided it would be much more fun to do what most of the A2 community is used to — I’d ignore all the great data just like your internal customers and instead use my intuition.

I’ve been covering technology and analytics for over a decade. I’ve seen trends come and go and I’ve seen the hype cycle first hand. If I can’t put my finger in the air and tell which way the wind is blowing, what good am I? So here we go. I’m making five predictions for state of analytics into the future.

In five years, inside the enterprise, analytics is just going to be called “management.” The drive for “data-driven management” and “data-driven organizations” is going to be so successful that we are going to stop talking about analytics as a separate discipline. Managers won’t ask what the BI or analytics said to make a decision. It will be ingrained in the process. Does that mean A2 is in for a name change? No, because there is still going to be a discipline for those that prepare the numbers. They are just going to fade into the background. I think the job of data scientist is much like “webmaster” was in the 90’s. We’re going to have less grandiose names while doing more work.

I’ve seen this already with “big data.” Just two years ago, CIOs would say to me “I need to invest in big data.” Now they say, “we’re investing in a new database to track customer data.” When the language around a thing changes from the hype word to the specific, it means it is part of the norm.

In five to 10 years (OK, maybe 20), personalized medicine will lead to diagnosing cancer with analytics. Multiple big data programs around cancer including the Wisdom study and the Precision Medicine Initiative are going to start leading to clues on how we can identify cancer earlier, based on things like your search terms, Facebook status, and fitness tracker data, rather than blood tests and mammograms. The clues will come in terms of early warning signs that we often ignore now as just feeling under the weather. Data will be able to combine genetic pre-disposition, health history, and our own data from daily life to see things before we see them ourselves. Of course, we’ll still need to take the test to confirm, but it will be the analytics that inspires us to the get the test.

In the next 10 years, a major sports franchise will hire a computer to be an in-game manager or general manager. Of course, a human will still have to do the interactive part like trade negotiations or telling a player they are being substituted. But someone will decide to trust in the analytics engine to be better than a human at making decisions like removing pitchers or punting versus “going for it” or for drafting players.

In 10 years, Hollywood will be routinely re-writing and re-shooting movie scripts based on biometric analysis of preview crowds. We’re already starting to track the data. It is only a matter of time until some fool thinks he can write a blockbuster using the data, and he’ll probably be right.

Driverless cars and trucks will nice, but it is the unimaginable amount of data from driverless cars and trucks that will allow us to finally live our “Jetsons-style” future we’ve been waiting for. Traffic flow through cities, combined with the more moderate data from personal fitness trackers and phones, will allow us to redesign city infrastructure like roads, water, WiFi, sewage, garbage, and everything else. Without the driverless car data, cities will look basically as they did in the 1980’s forever.

Which of my predictions look like they will happen and which make you think my crystal ball is broken? Tell me in the comments.

Source: http://www.allanalytics.com/author.asp?section_id=3618&doc_id=280191&print=yes

Citizen data scientist

The worldwide shortage of data scientists won’t end anytime soon. To try to compensate for the shortage, data discovery solutions are automating tasks that have traditionally been done manually by a data scientist, statistician, or other analytics expert. The confluence of trends is giving rise to a new role that Gartner calls a “citizen data scientist.”

A recent Gartner report defines a citizen data scientist as “a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.”

http://www.informationweek.com/big-data/big-data-analytics/citizen-data-scientists-7-ways-to-harness-talent/d/d-id/1321389

Block Chain technology

The terminology of this new field is still evolving, with many using the terms block chain (or blockchain), distributed ledger and shared ledger interchangeably. Formal definitions are unlikely to satisfy all parties —

  1. A block chain is a type of database that takes a number of records and puts them in a block (rather like collating them on to a single sheet of paper). Each block is then ‘chained’ to the next block, using a cryptographic signature. This allows block chains to be used like a ledger, which can be shared and corroborated by anyone with the appropriate permissions.

There are many ways to corroborate the accuracy of a ledger, but they are broadly known as consensus (the term ‘mining’ is used for a variant of this process in the cryptocurrency Bitcoin) — see below.

If participants in that process are preselected, the ledger is permissioned. If the process is open to everyone, the ledger is unpermissioned — see below.

The real novelty of block chain technology is that it is more than just a database — it can also set rules about a transaction (business logic) that are tied to the transaction itself. This contrasts with conventional databases, in which rules are often set at the entire database level, or in the application, but not in the transaction.

 

  1. Unpermissioned ledgers such as Bitcoin have no single owner — indeed, they cannot be owned. The purpose of an unpermissioned ledger is to allow anyone to contribute data to the ledger and for everyone in possession of the ledger to have identical copies. This creates censorship resistance, which means that no actor can prevent a transaction from being added to the ledger. Participants maintain the integrity of the ledger by reaching a consensus about its state.

Unpermissioned ledgers can be used as a global record that cannot be edited: for declaring a last will and testament, for example, or assigning property ownership. But they also pose a challenge to institutional power structures and existing industries, and this may warrant a policy response.

 

    1. Permissioned ledgers may have one or many owners. When a new record is added, the ledger’s integrity is checked by a limited consensus process. This is carried out by trusted actors — government departments or banks, for example — which makes maintaining a shared record much simpler that the consensus process used by unpermissioned ledgers. Permissioned block chains provide highly-verifiable data sets because the consensus process creates a digital signature, which can be seen by all parties. Requiring many government departments to validate a record could give a high degree of confidence in the record’s security, for example, in contrast to the current situation where departments often have to share data using pieces of paper. A permissioned ledger is usually faster than an unpermissioned ledger.

 

  1. Distributed ledgers are a type of database that is spread across multiple sites, countries or institutions, and is typically public. Records are stored one after the other in a continuous ledger, rather than sorted into blocks, but they can only be added when the participants reach a quorum.

    A distributed ledger requires greater trust in the validators or operators of the ledger. For example, the global financial transactions system Ripple selects a list of validators (known as Unique Node Validators) from up to 200 known, unknown or partially known validators who are trusted not to collude in defrauding the actors in a transaction. This process provides a digital signature that is considered less censorship resistant than Bitcoin’s, but is significantly faster.

    1. A shared ledger is a term coined by Richard Brown, formerly of IBM and now Chief Technology Officer of the Distributed Ledger Group, which typically refers to any database and application that is shared by an industry or private consortium, or that is open to the public. It is the most generic and catch-all term for this group of technologies.

    A shared ledger may use a distributed ledger or block chain as its underlying database, but will often layer on permissions for different types of users. As such, ‘shared ledger’ represents a spectrum of possible ledger or database designs that are permissioned at some level. An industry’s shared ledger may have a limited number of fixed validators who are trusted to maintain the ledger, which can offer significant benefits.

    1. Smart contracts are contracts whose terms are recorded in a computer language instead of legal language. Smart contracts can be automatically executed by a computing system, such as a suitable distributed ledger system. The potential benefits of smart contracts include low contracting, enforcement, and compliance costs; consequently it becomes economically viable to form contracts over numerous low-value transactions. The potential risks include a reliance on the computing system that executes the contract. At this stage, the risks and benefits are largely theoretical because the technology of smart contracts is still in its infancy, and some time away from widespread deployment.

 

 

“Distributed ledgers have the potential to be radically disruptive. Their processing capability is real time, near tamper-proof and increasingly low-cost. They can be applied to a wide range of industries and services, such as financial services, real estate, healthcare and identity management. They can underpin other software-and hardware-based innovations such as smart contracts and the Internet of Things.”

 

“their distributed consensual nature they may be perceived as threatening the role of trusted intermediaries in positions of control within traditionally hierarchical organisations such as banks and government departments.

 

DLT