7.4) evolving role of it in businessi… · website tracking services # of website visits, duration...

BEC-7 Miles CPA Review

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7.4) Evolving Role of IT in Business

I) Role of Big Data/Data Analytics in supporting business decisions

Data Analytics - Process of examining data sets so as to draw conclusions and generate business insight. Increasingly done with the aid of specialized systems and software

Big Data - Humongous volumes of data that includes both structured and unstructured data. Advanced techniques are required to use big data to enable enhanced insight/foresight, decision making and process automation Encompasses info from multiple internal and external sources such as transactions,

enterprise data, social media and multiple devices. Data sources may be classified as:

Structured sources - highly organized & readily searchable; e.g., database files, public indexes, spreadsheets

Unstructured sources - not organized & no predefined data pattern; e.g., emails, photos, videos, word documents, social media

Sources Examples of Data Structured Unstructured

Board & management Meeting minutes and notes ✓

Customer satisfaction survey

Feedback from priority customers ✓ ✓

Email Information relating to decision- making and entity performance

✓

Government-produced geopolitical reports

Population changes in emerging markets ✓

Manufacturer reports Emerging interest in products shipped from a competing manufacturer

✓

Marketing reports from website tracking services

# of website visits, duration on a page, conversions into customer purchases

✓

Public indexes Data from water scarcity index for beverage manufacturer or agriculture company considering new locations

✓ ✓

Social media and blogs Feedback & count of negative & positive comments on a company’s new product

✓ ✓

Dimensions of Big Data (4 V’s as described by IBM):

Volume - Scale of data (e.g., 2.3+ trillion gigabytes of data created everyday)

Velocity - Analysis of streaming data (e.g., NYSE captures 1 TB of trade info each session)

Variety - Different forms of data (e.g., Facebook, Youtube, Twitter)

Veracity - Uncertainty of data (e.g., business leaders don’t trust the info they use)

Using Technology - Data analytics historically relied on pre-defined patterns to convert data to info. Now, advances in cognitive computing, such as artificial intelligence, data mining, and machine learning can collect, convert & analyze large volumes of unstructured data into info that helps organizations make better business decisions. These advances, combined with human analysis, allow management greater insight E.g., Using unstructured info in decision-making via use of technology - A consumer retailer

uses artificial intelligence to gather insights about consumers through social media (e.g., purchasing behavior including historical patterns & preferences). These insights provide a better view to right inventory levels - thus, reducing risk of over- or understocking inventory

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Types of Business Analytics:

Source: Gartner Inc. [Gartner Analytic Ascendency Model]

Descriptive Analytics - Analyzing “What happened?”

Examining data or content; often performed manually

Characterized by traditional business intelligence (BI) and visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives

Diagnostic Analytics - Analyzing “Why did it happen?”

Used to drill down to root cause

Use of techniques such as drill-down, data discovery, data mining and correlations Predictive Analytics - Analyzing “What will happen?”

Use of historical patterns to predict business outcomes

Use of techniques such as regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling

Prescriptive Analytics - Analyzing “How can we make it happen?”

Applies advanced analytics techniques to make specific recommendations

Use of techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, machine learning

Based on optimization that helps achieve the best outcomes


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Advantages of advances in Data Analytics: Help organizations avoid “information overload” and use the huge amount of data now

available to its advantage Having more information pertinent to decision-making also reduces reliance on individual

experience and judgment in making those decisions May be able to:

Detect correlations in business performance that are not readily apparent

Identify likely trends in performance earlier

Evaluate key assumptions embedded into strategy, which in turn provides added insight in decisions on alternative strategies, business objectives, and setting performance targets

May help businesses in:

Generating new revenue opportunities

Effective marketing

Better customer service

Competitive advantage

Improved operational efficiency

Business Applications of Data Analytics - E.g., Few current applications

Marketing - targeted marketing, online advertising, cross-selling recommendations

Customer relationship management - Manage attrition and maximize customer value

Operations - Increase effectiveness & efficiency of business operations

Risk & Compliance - continuous monitoring, fraud detection

New Product Innovation - identify characteristics most valued by customers E.g., Few companies using Data Analytics

Amazon - Uses Big Data to recommend “What you may like” by analyzing the user’s search history

Ford - Aggregates data from 4 million vehicles for decision-making on issues such as how drivers use vehicles, what driving environment could help improve quality of the vehicle

Linkedin - Bank of data about people, how people make their money, what industries they work in, how they connect to each other

Finance & Accounting professionals need to develop the “Analytics mindset” Note that it is not just about technology or the advanced analytics skill-set that is needed to

produce analytics. What is also required is the focus on the behavioral alignment To drive better decisions, need to ask the right questions first and then seek answers in the

data. Suggested steps:

Ask the right questions

Extract, transform and load relevant data (i.e., the ETL process)

Apply appropriate data analytics techniques

Interpret and share the results with stakeholders


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Data Visualization - Presenting data in pictorial/graphical format (i.e., visual context). Makes it easier for users to identify patterns, trends & correlations

Use of data visualization tools & software

Most common use of data visualization is BI reporting tool (e.g., to generate automatic dashboards to track performance or forecasting charts to show sales)

Few types of graphs that may be used to communicate quantitative messages Time-series - e.g., Line chart reflecting sales trend over a period Ranking - e.g., Bar chart ranking of sales (measure) by salesmen (category) Part-to-whole - e.g., Pie chart reflecting market share of all firms Deviation - e.g., Bar chart to compare actual vs. budget sales Frequency distribution - e.g., Histogram (type of bar chart) reflecting number of years in

which sales growth is between intervals such as 0-10%, 11-20%, etc. Correlation - e.g., Scatter plot reflecting no. of products (X) and sales (Y) for a period Nominal comparison - e.g., Bar chart reflecting sales volume by product code Geographic or geospatial - e.g., Cartogram reflecting sales by regions

Considerations while using data visualization - Speed - due to the need to crunch large volumes of data

E.g., Use of grid computing approach (where many machines are used to solve a problem) is one of the approaches to increase speed

Understanding the data - need to determine the source of data, expectations of users and how data will be interpreted and used

Data quality - if data is not accurate and timely, it will not be able to produce accurate results for visualization

Displaying results that are meaningful - esp. given the fact that there are often variety of categories of information or large quantities of data

May use ‘binning’ or grouping data together by way of clustering into a higher- level view where smaller groups of data become visible is one way to manage large quantities of information

Dealing with outliers - While outliers may not be representative of the data, they may also reveal previously unseen and potentially valuable insights

Outliers can be spotted simply by looking at the data once it becomes graphical

Outliers can be removed or a separate graph can be made just for outliers to be analyzed further


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II) Artificial Intelligence, Machine Learning & Automation

Artificial Intelligence (AI) - Area of computer science that emphasizes the creation of intelligent

machines that work and react like humans

Mimics perception, cognitive functions and displays a consciousness like human minds (e.g. problem solving and learning skills)

AI has been successful in understanding speech, create real life simulations for training, self-driving cars, image recognition, interpreting complex data and even learning and playing chess based on moves of the opponent

AI research and application is being developed in the following areas: Searching and planning (e.g., playing chess) Reasoning and knowledge representation (e.g., IBM Watson playing games like Jeopardy

using natural language processing, information retrieval, knowledge representation, reasoning and machine learning)

Perception (e.g., speech recognition, facial recognition and object recognition) Move and manipulate objects (e.g., navigation and mapping) Natural language processing (e.g., Apple’s Siri, Google translate) Learning and machine learning (e.g., developmental robotics, algorithms to predict

outcomes, deep learning)

Weak AI vs. Strong AI Weak AI (also called Narrow AI) - Designed to be focused on a narrow task and to seem very

intelligent at it

E.g., Apple’s Siri, robots used in manufacturing, computer games Strong AI - Designed to be capable of all cognitive functions that a human may have (i.e., no

different than a real human mind)

E.g., Sophia (world’s first robot citizen!)


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Machine Learning - Part of AI where computers/machines have the ability to “learn” (just as humans do!) - i.e., computers/machines can act without being explicitly programmed

Tom M. Mitchell formally defined machine learning algorithms as: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E”

E.g., In data analytics, machine learning is used to devise complex models and algorithms for predictive analytics

Machine Learning algorithms follow: Infer/ Predict - Where once a data set is given the algorithm works on it to draw an

inference or a prediction Error - When the inference or prediction is compared to the desired outcome the machine

realizes the error and takes steps to correct it Train/ Learn- As the machine follows the process it learns and trains itself to get a more

optimal outcome every time

Various approaches of Machine Learning algorithms: Supervised learning

Input data is used by the machine in which it is required to make predictions. These get corrected when those predictions are wrong, and this process continues till the desired level of accuracy is achieved in the data

E.g., Predicting the housing costs in the state of Washington using square footage and distance from downtown

Linear regressions (continuous data) and logistics regressions (class of data) are used Unsupervised Learning

Input data is not labeled, the machine takes the data and puts it in different bins depending on criteria it thinks is common among the data put in one bin

E.g., Market segmentation in advertising Reinforcement learning

Machine learns from its own behavior, and takes action. When it fails it learns from its mistakes and if it wins or is rewarded it will go further to process new actions

E.g., In video games when the machine is the opponent it learns from its mistakes and next times improves on the move


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Infographic depicting future applications of Artificial Intelligence & Machine Learning -

Source: Frost & Sullivan


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Artificial Intelligence, Machine Learning & Automation in Accounting:

As technology is penetrating every aspect of the business, accounting & finance professionals can expect a shift towards strategic and analytic roles Automation will change the landscape for accountants and many areas are already seeing

changes. E.g.,

Data entry part of bookkeeping or historic recordkeeping has been eliminated by automation and cloud computing. For instance, there is no more manual keying bills or printing paper checks

Auto-reconciliations, contract and payment terms that auto-execute, AI routing cash applications and smarter billing is helping move away from manual activities

Virtual CFO services that leverage online finance tools to seamlessly integrate payroll, billing, financial statements and financing requirements to run businesses effectively and provide information real time

Filing tax returns and creating business reports Few new skills that accounting & finance professionals need to develop:

Advance capabilities in “Data” - governance, lifecycle, design, query, advanced analytics, visualization and story-telling

Develop leadership & strategy management skills, which include critical thinking and judgment

In essence, be a future-proof accountant with the skills to turn:

- Data to decisions

- Information to insights

- Forecasts to futures valued by stakeholders

AI and Machine learning have been adopted by businesses to remain competitive to deliver real-time insights, enhance decision making and catapult efficiency Accountants can shift focus from low-value data extraction and preparation activities to

using institutional knowledge and turn focus on business process improvement E.g., Use AI to extract information from invoices or purchase orders to enter into accounting

and auditing systems. This will enable accountants to analyze significant amounts of data, and deliver more analysis and insights

AI (being a new technology) introduces new risks to business; therefore, monitoring and control activities need to be enhanced for AI systems

As AI starts being used for preparation of financial statements, the auditing profession will need to develop standards to keep a check and balance and ensure reliability for the information coming from these systems


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III) Digital Business Models

Digital and online services are evolving at an unseen speed. After 20 years of online activity, digital technologies have fundamentally changed the way we live, work and play. And this is merely the beginning. We cannot imagine what’s coming at us in the next few decades. Exciting, but all of these evolutions have a huge impact on our lives. And ultimately on the way we do business. Digital is disruptive. 10 Hyper-Disruptive Business Models (as covered by Jo Caudron and Dado Van Peteghem in their book Digital Transformation):

The Subscription Model (Netflix, Dollar Shave Club, Apple Music) Disrupts through “lock-in” by taking a product or service that is traditionally purchased on

an ad hoc basis, and locking-in repeat custom by charging a subscription fee for continued access to the product/service

The Freemium Model (Spotify, LinkedIn, Dropbox) Disrupts through digital sampling, where users pay for a basic service or product with their

data or ‘eyeballs’, rather than money, and then charging to upgrade to the full offer. Works where marginal cost for extra units and distribution are lower than advertising revenue or the sale of personal data

The Free Model (Google, Facebook) Disrupts with an ‘if-you’re-not-paying-for-the-product-you-are-the-product’ model that

involves selling personal data or ‘advertising eyeballs’ harvested by offering consumers a ‘free’ product or service that captures their data/attention

The Marketplace Model (eBay, iTunes, App Store, Uber, AirBnB) Disrupts with the provision of a digital marketplace that brings together buyers and sellers

directly, in return for a transaction or placement fee or commission

The Access-over-Ownership Model (Zipcar, Peerbuy, AirBnB) Disrupts by providing temporary access to goods and services traditionally only available

through purchase. Includes ‘Sharing Economy’ disruptors, which takes a commission from people monetising their assets (home, car, capital) by lending them to ‘borrowers’

The Hypermarket Model (Amazon, Apple) Disrupts by ‘brand bombing’ using sheer market power and scale to crush competition,

often by selling below cost price

The Experience Model (Tesla, Apple) Disrupts by providing a superior experience, for which people are prepared to pay

The Pyramid Model (Amazon, Microsoft, Dropbox) Disrupts by recruiting an army of resellers and affiliates who are often paid on a

commission-only model

The On-Demand Model (Uber, Operator, Taskrabbit) Disrupts by monetising time and selling instant-access at a premium. Includes taking a

commission from people with money but no time who pay for goods and services delivered or fulfilled by people with time but no money

The Ecosystem Model (Apple, Google) Disrupts by selling an interlocking and interdependent suite of products and services that

increase in value as more are purchased. Creates consumer dependency.


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Few strategies to respond to disruptors for businesses who are getting disrupted (as enumerated by IMD’s Professor Michael Wade, co-director of the IMD Leading Digital Business Transformation course):

The Block Strategy - Using all means available to inhibit the disruptor. These means can include claiming patent or copyright infringement, erecting regulatory hurdles, and using other legal barriers

The Milk Strategy - Extracting the most value possible from vulnerable businesses while preparing for the inevitable disruption

The Invest in Disruption Model - Actively investing in the disruptive threat, including disruptive technologies, human capabilities, digitized processes, or perhaps acquiring companies with these attributes

The Disrupt the Current Business Strategy - Launching a new product or service that competes directly with the disruptor, and leveraging inherent strengths such as size, market knowledge, brand, access to capital, and relationships to build the new business

The Retreat into a Strategic Niche Strategy - Focusing on a profitable niche segment of the core market where disruption is less likely to occur (e.g. travel agents focusing on corporate travel, and complex itineraries, book sellers and publishers focusing on academia niche)

The Redefine the Core Strategy - Building an entirely new business model, often in an adjacent industry where it is possible to leverage existing knowledge and capabilities (e.g. IBM to consulting, Fujifilm to cosmetics)

The Exit Strategy - Exiting the business entirely and returning capital to investors, ideally through a sale of the business while value still exists (e.g. MySpace selling itself to Newscorp)


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IV) Cloud and Mobile Computing

Cloud computing - Practice of using a network of remote servers hosted on the Internet to store,

manage, and process data, rather than a local server or a personal computer. Characteristics:

Uses a network of remote servers to store, manage and process data which is hosted on the internet

Provides on-demand network access to shared pool of resources such as networks, servers, applications etc.

Different types of cloud computing include private clouds, public clouds, community clouds, virtual private clouds, hybrid clouds etc.

The user has data stored with a cloud service provider and uses software (software as a service) at the cloud service provider’s website, which can be accessed over the Internet

Cloud service delivery models are software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS)

Helps eliminate the need to purchase and maintain an in-house infrastructure

Capabilities can scale rapidly outward and inward to meet with demand appropriated in any quantity at any time

Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service

Cloud computing service operating models:

Software as a Service (SaaS): Providers of the software application is responsible for running it on a cloud infrastructure Applications are accessible from various client devices through

Thin client interface such as a web browser (e.g. web-based email)

A program interface Client does not manage or control the underlying cloud infrastructure, including network,

servers, operating systems, storage, or even individual application capabilities

Platform as a Service (PaaS): The provider supports consumer-created or acquired applications created using

programming languages, libraries, services, and tools supported by the provider Consumer does not manage or control the underlying cloud infrastructure, including

network, servers, operating systems, or storage, but has control over the deployed applications and possibly configuration settings for the application-hosting environment

Infrastructure as a Service (IaaS) The provider provides processing, storage, networks, and other fundamental computing

resources in which the consumer is able to deploy and run arbitrary software, which can include operating systems and applications

The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, and deployed applications and possibly limited control of select networking components (for example, host firewalls)


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Cloud computing service deployment models:

Private cloud: Used by a single organization comprising multiple users May be owned, managed, and operated by the organization, a third party, or some

combination of them May exist on or off premises

Community cloud: Used by a specific community of consumers from organizations that have shared concerns

(for example, mission, security requirements, policy, and compliance considerations) May be owned, managed, and operated by one or more of the organizations in the

community, a third party, or some combination of them May exist on or off premises

Public cloud: Used by the general public. It may be owned, managed, and operated by a business,

academic, or government organization, or some combination of them Exists on the premises of the cloud provider

Hybrid cloud: Made up of two or more distinct cloud infrastructures (private, community, or public) May be unique entities but are bound together by standardized or proprietary technology

that enables data and application portability (e.g. cloud bursting for load balancing between clouds)


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V) Blockchain and Cryptocurrencies

Blockchain -

Most business transactions need some kind of middlemen like banks, insurance companies etc. Blockchain removes the need for a middleman and connects consumers & suppliers directly Crytocurrency is the most popular currency used in Blockchain technology

Blockchain process - Transactions initiated are represented as a ‘block’ or an online record (e.g., A wants to send

money to B) The “block” is broadcasted to every party in the network to all parties who are connected Once the parties in the network authenticate the transaction, the “block” is added to the

“chain” and transaction is processed (e.g., money transfer from X to Y) Note that “blocks” on the “chain” are secure and permanent records that cannot be deleted

[Image: Financial Times]

Characteristics of blockchain - Basically, blockchain consists of “blocks” (i.e., online records) that are “chained” (i.e., linked and secured using cryptography) Each block is connected with the previous block through links that contain timestamp and

transaction data Takes the form of a ledger that is decentralized on a peer to peer network Users can confirm transactions without a central authority certifying them Buyer and seller interact directly without needing verification by third-party intermediary Transaction record is created in a blockchain, but identifying information is encrypted, and

no personal information is shared Blocks of transactions, as well as individual transactions, are continuously validated so

committing frauds is very difficult Algorithms also incorporate an ID for each buyer and seller in a transaction, adding those

IDs to the block


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Advantages of Blockchain - High transparency in the transactions, which can be tracked accurately The ledger are permanent record (which cannot be altered) Every core transaction is processed just once (in one shared electronic ledger), thus,

reducing redundancy and delays Huge cost savings versus maintain physical records and builds collaborative technology

between companies doing business The ledger being distributed, publicly verified, and nearly real-time data mining, and records

verification reduces time and effort spent on reconciliation of information

Disadvantages of blockchain - Technology is complex Regulatory clearance and implications are unknown Implementation and training is complicated

Blockchain uses - Blockchain technology is mostly explored in the financial services sector currently and spreading into healthcare, legal, insurance, telecommunications, etc. E.g. settling of stock trades, patient’s health records, insurance Few emerging categories for blockchain use cases [Image: Peter Bergstrom]:


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Cryptocurrency -

Currency which is ‘mined’ by miners (who are the network of parties that authenticate the transactions) Miners are generally computer programmers who get rewarded by getting digital currency Basically, mining serves two purposes:

Authenticating transactions and

Generating digital currency. Mining needs a lot of resources and is intended to be an arduous task. Individual blocks

contain the proof of work of the miners, and is verified at every time a new block is generated. Miners ensure that the transaction is secure and processed properly and safely. The miners add transaction records to public ledger

Characteristics of Cryptocurrency: Just like currency, cryptocurrency is a medium of exchange Created and stored electronically Does not have intrinsic value and cannot be redeemed for another commodity Central bank does not determine supply for cryptocurrency, the network is distributed and

decentralized Cryptocurrencies provide cheaper and faster peer-to-peer payment options without the

need to provide personal details Regulatory requirements for cryptocurrencies are still not well established and how

different countries are going to react is yet to be seen

E.g., Bitcoin, Ethereum, Litecoin


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Bitcoins - Cryptocurrency that is the first digital currency that was introduced in 2009

Bitcoins are made by ‘mining’ them, and can be created only at a constrained rate On an average it takes about 10 minutes per coin and each new bitcoin is slightly more

difficult to create than the one that came before

Mining is the digital version of record keeping. Miners keep the blokchain complete and unalterable by repeatedly verifying and collecting newly broadcast transactions into a new group of transactions called a block

Each block contains a cryptographic hash of the previous block Secure hash algorithm 2 (SHA-2) is used to link the block to the previous blocks creating

blockchain Used as a medium of exchange like the $ or any other currency, but has no intrinsic value Bitcoin is authenticated by the peer network that produced it Bitcoin exists on the distributed ledger which uses public-key encryption Public key encryption is virtually impossible to break, because a message can be unlocked

only when a public and a private element (held only by the recipient) are linked Every time a bitcoin is created or changes hands, the ledger automatically creates a new

transaction record composed of blocks of data, each encrypted by altering (or “hashing”) part of the previous block

Source: www.pointofsync.com

http://www.pointofsync.com/


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