What is the future of data analytics gone beyond user experience?

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What Is The Future Of Data Analytics Going Beyond User Experience?

In the video below on CxO Talk, I asked Giorgos Zacharia, CTO of Kayak, about what comes next after data-based user experience improvements.

In my view, as organizations continue to collect data to improve user experiences, we have think beyond what is possible just to improve a product. Here are few suggestions:

  1. Collect data from your supply chain(s) to improve operations
  2. Collect data from your employees to improve employee experiences
  3. Collect data outside of your organization to see how it can improve you

5 Questions to Ask About Prescriptive Analytics

Prescriptive analytics is used for performance optimization. This optimization is accomplished by using a variety of statistical and analytical techniques to identify the decisions that need to be taken in order to achieve the desired outcomes. The data sources used for the determination of outcomes can range from structured data (e.g., numbers, price points, etc.), semi-structured data (e.g., email, XML, etc.) and unstructured data (e.g., images, videos, texts, etc.).

If done correctly, Prescriptive Analytics is the Holy Grail of analytics. However, if done incorrectly, it can result in misinformed decisions that can be outright dangerous. Individuals and organizations have to understand that even if the data is correlated that does not mean that there is some sort of causation. A general example of this is when in a news report, the host(s) says that the survey has shown that x is correlated with y but then they go on how y was caused due to x. This is simply what I call “jumping the data gun” and organizations that are not aware of this can fall into this trap.

Another thing to be aware of is that after the Prescriptive Analytics gives you certain courses of action and you apply those actions, keep track of how well your Prescriptive Analytics is performing as well. In other words, you have to measure the performance of your performance optimization ways. The reason to do this is that over time you can see if the models presented by your Prescriptive Analytics engine are worth following, re-doing or dumping.

To get you started, here are a few questions to ask:

Today

Tomorrow

Who uses prescriptive analytics within, across and outside your organization?Who should be using prescriptive analytics within, across and outside your organization?
What outcomes do prescriptive analytics tell you?What outcomes prescriptive analytics should tell you?
Where is the data coming from for prescriptive analytics?Where should the data become from for prescriptive analytics?
When prescriptive analytics is used?When prescriptive analytics should be used?
Why prescriptive analytics matter?Why prescriptive analytics should matter?

When you are asking the above questions, keep in mind that Prescriptive Analytics uses data to create a model (aka a data version of the world) that is used by individuals and organizations to make real-world decisions. But if the model itself is flawed then you are bound to get answers that although might look visually appealing are completely wrong. It is not all doom and gloom though. In fact, Prescriptive Analytics is used in determining price points, expediting drug development and even finding the best locations for your physical stores. Companies like Starbucks have been using Prescriptive Analytics in the last few years to determine the best locations for their next coffee stores. Interestingly, some have claimed that wherever Starbucks goes, the real-estate prices also increase. While there is some correlation between a Starbucks coffee store opening with increased real-estate prices but this does not mean that because of Starbucks coffee stores the real-estate prices increase.

Analytics Trophies

References:

  1. 5 Questions to Ask About Business Transformation
  2. 5 Questions to Ask About Your Information
  3. Starbucks Tries New Location Analytics Brew

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5 Questions to Ask About Your Big Data

In statistics, a hypothesis is proposed and then data samples are collected to prove or disprove the hypothesis with acceptable confidence levels. For example, let’s say that all our customers are aware of all our product lines. Basically, there are two ways of assessing our hypothesis that includes: (1) Proving our hypothesis and (2) Disproving our hypothesis.

The first way to proving our hypothesis is that we communicate with all of our customers and inquire if they know all our product lines. The second way is to communicate with as many customers as possible until we come across any customer that does not know all our product lines. From this example, we can see that if we find even one customer then that disproves our hypothesis. Thus, this is the reason why in statistics, sometimes it is easier to find an exception to disproving a hypothesis rather than proving it.

Big Data, on the other hand, inverts the generally acceptable process from hypothesis then data sample collection to data collection then a hypothesis. What this means is that Big Data emphasizes collecting data first and then coming up with a hypothesis based on patterns found in the data. Generally speaking, when we talk about Big Data, we are concerned with the 3 Vs that include:

  • Volume – Amount of data
  • Velocity – Rate of data analysis
  • Variety – Different data sources

Some have indicated that we need to go beyond just the above three Vs and should also include:

  • Viscosity – Resistance to the flow of data
  • Variability – Changes in the flow changes of data
  • Veracity – Outlier data
  • Volatility – Validity of the data
  • Virality – Speed at which data is shared

I would take the Big Data concept a bit further and introduce:

  • Vitality – General and specific importance of the data itself
  • Versatility – Applicability of data to various situations
  • Vocality – Supporters of data-driven approaches
  • Veto – The ultimate authority to accept or reject Big Data conclusions

For a metrics-driven organization, a possible way to determine the effectiveness of your Big Data initiatives is to do a weighted rating of the Vs based on your organizational priorities. These organizational priorities can range from but not limited to increasing employee retention rates, improving customer experiences, improving mergers and acquisitions activities, making better investment decisions, effectively managing the organization, increasing market share, improving citizens services, faster software development, improving the design, becoming more innovative and improving lives. What all of this means is that data is not just data but it is, in fact, an organization’s most important asset after its people. Since data is now a competitive asset, let’s explore some of the ways we can use it:

  • Monte Carlo Simulations – Determine a range of scenarios of outcomes and their probabilities.
  • Analysis of Variance (ANOVA) – Determine if our results change when we change the data
  • Regression – Determine if data is related and can be used for forecasting
  • Seasonality – Determine if data shows the same thing occurring at the same intervals
  • Optimization – Getting the best possible answer from the data
  • Satisficing – Getting a good enough answer from the data

Now that we understand what is Big Data and how it can be used, let’s ask the following questions:

Today

Tomorrow

Who is capturing data?Who should be capturing data?
What is the lifecycle of your data?What should be the lifecycle of your data?
Where is data being captured?Where should data be captured?
When is data available for analysis?When should data be available for analysis?
Why data is being analyzed?Why data should be analyzed?

Having discussed the positives of Big Data, we have to realize that it is not a panacea and has its negatives as well. Some of the negative ways data can lead to bad decisions include: (1) Data is correlated but that does not imply cause and effect, (2) Data shows you pretty pictures but that does not imply it is telling you the truth and (3) Biases can affect data anywhere from capturing to analysis to decision-making.

In conclusion, what this means is that the non-distorted quality, understanding, and usage of data is the difference between just getting on the Big Data bandwagon or truly understanding how data can fundamentally change your organization.

Big Data Vs

References:

  1. Realizing the Promise of Big Data
  2. Beyond the three Vs of Big Data
  3. 5 Factors for Business Transformation
  4. 5 Questions to Ask About Your Business Processes
  5. 5 Questions to Ask About Your Information
  6. 5 Questions to Ask About Customer Experiences
  7. 5 Observations on Being Innovative (at an organizational level)
  8. Where is my Big Data coming from and who can handle it

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5 Questions to Ask About Your Information Security

The term information security is used to describe the practices, methodologies, and technologies that are used to protect information physically (e.g., locked doors, security guards, etc.) and in cyberspace (e.g., firewalls, anti-viruses, etc.). In order to accomplish this, we determine information confidentiality (e.g., who can access the information), information integrity (e.g., is the information from a reliable source) and information availability (e.g., would the information be available in time to people who are authorized to use/see it).

According to Gartner, by 2015 the spending on information security around the globe would reach $76.9 billion. To put this number into perspective, this amount of money is close to what the US Federal government spends on technology in one year. By looking at this, in the near future, more money would be spent on securing personal and organizational information than actually creating information systems. But despite the importance of information security and its effects on individuals and organizations, very few people understand the kinds of threats that are out there. Security threats are always evolving and in the digital century, geography is not a limitation. Individual and organizational information can be potentially compromised from a local intruder to someone sitting on the other side of the globe. Thus, before you can mitigate information security risks, understand what is out there. Here is a non-exhaustive list of how information security can be compromised:

  • Adware – Pay to remove advertisements.
  • Bacteria – Overwhelms computer resources by making copies.
  • Botnets – A network of compromised systems.
  • Bots – Derived from robots and refers to automated processes.
  • Buffer Overflow – A program goes beyond the boundary of the buffer.
  • Clone Phishing – Legitimate email resent with malicious link/attachment.
  • DDoS – Multiple systems attack a single target.
  • DNS Attacks – Determine types of devices in the network.
  • Easter Eggs – Hidden code in the software to show control.
  • Emerging Technologies –Security is not considered in new technologies.
  • Evil-Twin Wi-Fi – Impersonates an access point (e.g., router).
  • Exploits – Vulnerabilities in scripts, servers, browsers, routers, computer networks, devices, software, and hardware.
  • Hardware Attacks – Exploits system bus, a peripheral bus, chips, power/timing, interrupts and RAM.
  • Human Error – Unintentional legitimate errors caused by people.
  • ICMP Scanning – Identify open ports (e.g., port 81).
  • Keylogger – Track keystrokes when logging on to legitimate sites.
  • Link Manipulation – The destination link is different than what is displayed.
  • Logic Bombs – Performs some action when certain conditions are met.
  • Malware – Malicious code.
  • Masquerading – Pretends to be authorized access.
  • Metamorphic – Code that modifies itself.
  • Network QoS – Service interruptions and performance issues.
  • Old technology – Outdated technology that is too costly to replace.
  • Pharming – Redirecting web traffic to a fake site and more sophisticated.
  • Phishing – Emails/instant messages asking to click a link/attachment, sign up for some kind of service and/or take you to a site that looks legitimate.
  • Phone Phishing – Call to ask for information.
  • Polymorphic – The same underlying code used for multiple purposes.
  • Rogue Wi-Fi – Compromised wireless access points (e.g., routers).
  • Script Kiddies – Amateur use of scripts developed by professionals.
  • Social Engineering – Psychologically manipulating people.
  • Spear Phishing – Directed toward specific individuals or organizations.
  • Spyware – Typically free software that collects information about you.
  • SQL Injection – SQL code is entered into the input fields of a database.
  • Trapdoors – Secrets in the code that allow access to the system.
  • Trojan Horses – Impersonates another software, prompts to install software and prompts to go to a certain site.
  • Viruses – Adds code to an uninfected copy of the host program in the network and then replicates itself.
  • VoIP Attacks – Software and hardware exploit in Internet telephony.
  • VPN – Only as secure as the most unsecure system in both ends of the network.
  • Weather – Mother Nature and lack of disaster recovery.
  • Whaling – Attacks directed at high profile individuals and organizations.
  • Worms – Copies itself across the network, runs by itself and does not need a host.
  • Zero-Day Exploits – Vulnerabilities in software unknown to anyone.

Now that we understand the potential risks that are out there, let’s look at what motivates people to do this. While there are many theories in what drives human motivation, for our purposes we look at the following two frameworks used by the top clandestine organization in the world. These frameworks are:

  • MICE looks at human motivation in terms of Money (e.g., cash, stocks, insider information, etc.), Ideology (e.g., religion, patriotism), Coercion or Compromise (e.g., blackmail) and Ego or Excitement.
  • RASCLS looks at human motivation in terms of Reciprocation (e.g., feel obligation to repay), Authority (e.g., prestige), Scarcity (e.g., supply vs. demand), Commitment and Consistency (e.g., trustworthy flip-flopper vs. untrustworthy but consistent), Liking (e.g., share same attributes) and Social Proof (e.g., correct behavior).

In order to understand the complexities of information security and motivations behind it, let’s ask the following questions:

Today

Tomorrow

Who is responsible for information security?Who should be responsible for information security?
What happens when information is compromised?What should happen when information is compromised?
Where is information security a priority?Where should information security be a priority?
When is information security thoroughly reviewed?When should information security be thoroughly reviewed?
Why information security was compromised in the first place?Why information security would continue to be compromised in the future?

When you are asking the above questions across all levels of the organization, keep in mind that information security is not something that you just “bolt-on” at the end but in fact, it should be a top priority at every juncture of your organizations. Thus, information security spans across people, processes and technologies and simply paying lip service do not help anyone in the long run.

While there are many laws, regulations, and guidelines to safeguard information but they do not mean much if you cannot apply them across and within your ecosystem of vendors, partners, suppliers and any external entities. In short, information security is a collective effort that requires organizations to be self-aware from the lowest ranks to the highest executives.

Information Security Views
Information Security Views

References:

  1. http://www.gartner.com/newsroom/id/2828722
  2. https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/csi-studies/studies/vol.-57-no.-1-a/vol.-57-no.-1-a-pdfs/Burkett-MICE%20to%20RASCALS.pdf

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