What does this number represent…. 84.76% of stats are made up on the spot.

Funny, but the truth is most of the time when you are given stats their value is no greater than the number on the screen.

Quantitative Data - numeric data - can be discrete or continuous

Qualitative Data - limited number of descriptive alternatives (discrete data)● nominal unordered / no natural order (these are labels)● ordinal if an ordering exists (scaled)

Discrete Data - data that can only take certain values (limited variations)

Continuous Data - data that can be measured on a continuum or scale (nearly any numeric value and meaningfully subdivided into finer increments)

Volume - this is purely the size of the data collected. Size is arbitrary

depending on industry or sources and must also take time into

consideration. Remembering what is huge today, is much smaller with the

technology of tomorrow.

Variety - is more of a technical measure of the format or type of data.

Structured (5%) is ordered / normalised data found in spreadsheets and

relational databases. Semi-structured occurs between structured and un,

where there is a framework but it is flexible such as XML used for inter app

/ device communication. Unstructured is exactly as it sounds, is comprised

of data from a disparate system of sources and does may contain no or

loose relationships. Audio / video are examples, while they are comprised

of bits and bytes of data (1s and 0s) there is not structured framework

(even if there could be a pattern).

Velocity - the rate at which data are generated and the speed at which it should be analyzed and acted upon.

Veracity - the unreliability inherent in some sources of data meaning the

need to deal with imprecise and uncertain data is another facet of big

data, which is addressed using tools and analytics developed for

management and mining of uncertain data.

Variability - the variation in the data flow rates (periodic peaks and

troughs) and the fact that big data are generated through a myriad of

sources. This creates the need to connect, match, cleanse and transform

data received from different sources.

Value - characterized by relatively “low value density”, the data received

in the original form usually has a low value relative to its volume. High

value can be obtained by analyzing large volumes of such data.

Here are some statistics… considering what was said earlier, take it all with a grain of salt. This data was from 215

Remain Competitive - you may or may not be in an innovative industry but rest assured if there is not a competitor using data innovatively they are looking at it. It is a case of use it or lose it

Increase Customer Loyalty - this is applicable for both B2B and B2C, imagine knowing what your customer wants before they do?

Stay Relevant - all industries are changing as a result of the internet, connectivity, disruption (as the old joke goes, can you imagine a world without lawyers, well this is a very strong possibility). Being aware of your data and how you can use it will allow you to stay ahead of the curve or at least prepare for disruption or more savvy players.

Key Concepts to go over this today

Data Principles - what you should be doing

Data Strategies - how you should be doing it

Using Customer Data - leveraging what you have

Context - This is key for adding value to your data. One of the critical activities that needs to happen before analysis is understanding the data. This builds trust in the value and adds meaning to the insights. Being handed a spreadsheet by an analyst and having been explained X is up and Y is down add little value. If you understand how the data segments that were analysed were created you can make better informed decisions.

Trend Analysis - So often I still hear requests for things like “how many sales did we make last month” or “what was our revenue for the month”, without understanding past performance or having predicted future performance, current performance means nothing. 1B in sales for the year (good or bad?) if last year was 500M then good, if the year before was 2B then not great.

Approach - What would be a bigger mistake than not using data would be to walk away today and either hire a team or form a team to start looking at your data. Without a clear plan, goals, expectations and execution strategy data analysis is as effective as running your business with an abacus

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More is Better - Big Data is not just a buzzword it is a description of what is occurring every day. The Internet of Things (meaning everything is connected these days) generates incredibly high volumes of data at increasing velocity. Most machines these days are connected or can be connected to the Internet or at least produce usage statistics. Whether machine or customer information, if used correctly, can a) prevent disaster, b) improve efficiency and c) provide critical data for forecasting performance to avoid contraction and facilitate growth.

Subsets - It is very important to be aware of and ensure the pool of data being analysed is relevant to the “question”. Greater insight is gained by looking at specific data subsets, further insight is gained from comparing and contrasting those insights.

Data’s not the answer - when training people on analysis, my suggestion is to be a data detective, find anomalies and or trends and used these to uncover insights. If your teams are providing you with data then they are not doing their jobs. The data should just be there as a proof. When my teams release reports, the data is in the appendices, what matters are the insights and more importantly the recommendations to the customer.

Example - What we know is customers. Our customers who look at their customer behaviour (last week, month, quarter, year and comparison

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periods) and use this to forecast future sales allows them to decrease production / delivery time, reduce wastage, decrease stock levels without fear of shortage and more importantly predict customer desire, so they can proactively service their customers and cement their position and primary and or preferred supplier.

Manufacturing Example - data sources (equipment, workforce, customer order patterns, industry trends, policy changes - like the CN Environmental policy, suppliers) to name a fewMost successful / savvy businesses have data and are aware of all the above. Very savvy businesses have modelled the future patterns. Understanding the lifespan and effectiveness of your equipment, compared with the materials you are using and predicted customer order patterns allows you to easily produce premium vs acceptable products (charge more), schedule machine maintenance with the least amount of impact or more importantly be ahead of curve / innovative when it comes to shifts in the industry, critical changes to policy or general global up or down turns

Business Objectives - use what you are trying to achieve with your organisation to help identify what you need, the data will be there

Data Sources - include all areas of the organisation, online and offline sources, key personnel and stakeholders

Execution - these are the tools, products, solutions, people and processes you will need

To be a truly Data Driven organisation adoption needs to be top down. Analysis, data driven decisions and statistical information must be celebrated and encouraged. Many experienced business people will want to use their gut, but the gut is just a number cruncher based on previous wisdom and experience. This works where there is stability, but falls over where there is disruption.

What most organisations do now is review (monthly / annual reports) which is important to review and measure if goals and targets we met. With effective analysis techniques the historical results can be used to undertake future prediction (forecasting).

Most organisations undertake forecasting; sales, budgets, resources.

With an effective data strategy and modelling, AI or machine learning can be used to more accurately predict organisation wide requirements. The greater and more disparate volume of data integrated allows for a more complete picture for prediction (industry trends, world events, global market movements).

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A quick review of just some data sources, there are many many more…..● HR systems● Work stations● Digital presence● Focus groups● Customer feedback

…. to name a few.

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Cyclic, and continuous.

BOA - analysis or insight without alignment to the business direction and goals can be a waste of time or counter productive. Analysis can be undertake to look for change or improvement, but it should all be aligned with the direction of the business.

Buy In - one person pushing for change from the top (MD / CEO) or bottom (analyst) will not be able to enact change. People need to understand the WHY as well as buy into the vision or direction.

Data - here is where the magic happens, you want as much Big Data as you can get from your organisation, meaning Volume, Velocity and Variety. Think about key systems, key processes, key areas and key interfaces and look for data collection from these.

Action - analysis and insights are only the first step, execution and adoption are more critical. A clear roadmap of change, including goals, milestones, budgets and timelines is critical for an effective rollout to make it happen and ensure you are effectively investing financially and resources.

CI - a truly innovative and forward thinking organisation will cyclically execute this process to ensure they never fall behind or miss

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opportunities.

Reinforce the importance of what can be achieved with a good data strategy and execution of the strategy.

You need to start thinking about data as more than just a nice to have but as a resource that will help build / grow / strengthen your businesses.

Most use data now (monthly / annual financial reports, HR performance reports, waste management reports) the next step is to start thinking about your data holistically and how what seem like disparate systems can be analysed together to provide deeper insight.

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Describe some of the benefits by organisational departments

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Customer data is a key source of information for your organisations, many businesses provide millions of dollars in incentives just to get you to give you information about yourself

Using key products, platforms, and system integration you should be able to build a reasonable profile for your customer

But beware, a customer does not tell the story, it is important to look at customer segments to truly get value and insight

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Personalisation / persona development - this can be used to ensure your customer has the exact visit experience they were looking for. This also means you can use similar customers to undertake analysis and provide like experiences.

CLV - understanding your customer lifetime value provides you the platform to know when to squeeze and when to over deliver. CLV provides you the critical when ranking customers and identifying key success metrics and industries.

PPI - having this insight will help you with production, marketing and forecasting. Knowing the patterns of your customers and defined customer personas will allow you to better predict your sales and ordering pattern into the future as well as provide a baseline for performance measurement.

Cross-sell / up-sell - your data can be used to build associations with like products as well as predict potential changes in behaviour or need, allowing you to provide the right incentive at the right time to increase average order value.

Reward Loyalty - there are many different ratios for how hard it is to win a new customer (8 times harder), but we are all aware that you definitely invest more time into attracting new customers than acquiring repeat

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business. Knowing your customer behaviour, patterns and CLV allows you to reward their patronage and build the all important loyalty while ensuring you are still getting a strong return from their business.

Selling / sharing / not protecting - depending on the laws of the country or the digital agreements you have with your customers, if not getting into a great deal of trouble, you at least lose their trust which will most likely end up in losing their business.

Ignore it - you have it, we all have it, if you are not analysing your customer data you are all but throwing away revenue.

Analysing in aggregate - use attributes to build customer personas, looking at the data subsets and performing analysis based on the segmentation will provide a great deal more insight. Analysis of aggregate data only gives you top line metrics but simply segmenting by male and female or first time vs return customers will deliver a great deal more insight.

Be complacent - your business changes, your market changes, your industry changes, the world changes. Disruption is here and is only permeating deeper and deeper into all industries. Walking away from here and commissioning an analysis of your customers which will take you through the next 10 years is not effective enough. Remember 10 years ago the iPhone had just been released.

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1. Patch, Patch, PATCH! - Set up your computer for automatic software and operating system updates. An unpatched machine is more likely to have software vulnerabilities that can be exploited.

2. Install protective software - Find a paid or free version of antivirus software and install. Ensure your business servers are configured and monitored for irregular behaviour and processing.

3. Choose strong passwords - Strong passwords use a combination of letters, numbers, and special characters to create a mental image or an acronym that is easy for you to remember. Create a different password for each important account, and change passwords regularly. LastPass can manage all of your passwords for you -- and it's free!

4. BACK UP on a regular basis! - Regular, scheduled backups can protect you from the unexpected. Keep a few months' worth of backups and make sure the files can be retrieved if needed. Download and install CrashPlan and learn how to back up your system.

5. Control access to your machine - Don't leave your computer in

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an unsecured area, or unattended and logged on, especially in public places. This includes Athena clusters and Quickstations. The physical security of your machine is just as important as its technical security.

6. Use email and the internet safely - Ignore unsolicited emails, and be wary of attachments, links, and forms in emails that come from people you don't know, or which seem "phishy." Avoid untrustworthy (often free) downloads from freeware or shareware sites. Learn more about spam filtering.

7. Use secure connections - When connected to the internet, your data can be vulnerable while in transit. Use remote connectivity and secure file transfer options when off campus.

8. Protect sensitive data - Reduce the risk of identity theft. Securely remove sensitive data files from your hard drive, which is also recommended when recycling or repurposing your computer. Use the encryption tools built into your operating system to protect sensitive files.

9. Use desktop firewalls - Mac and Windows computers have basic desktop firewalls as part of their operating systems. When set up properly, these firewalls protect your computer files from being scanned.

10. Most importantly, stay informed - Stay current with the latest developments for your operating systems. Subscribe to security newsletters or appoint a security position.

Real insights. Actionable insights. Reports which tell story. Question-answering reports.

Do not get overwhelmed, look at your data as opportunity.