DataPlatformSummit: Delighted to be returning to present on #AI and #PowerBI in India!

I am delighted to be presenting at Data Platform Summit 2019 in Bengaluru, India in August 2019!

What is Data Platform Summit? The conference packs a punch of learning and information, and you will get actionable knowledge that you and your company can benefit from – right away! The conference is a well-put together plan of 3 Days of Pre-Conference Training , 3 Days of Conference Learning, 7 Key Technology Tracks, 50+ Global Subject Matter Experts & Industry leaders, 100+ Sessions, 300+ Companies, 1000+ Attendees, 90+ Subjects, 30+ Technologies & an unimaginable flow of intelligence & expertise from friends and colleagues in the industry from around the world.

My sessions are listed here and I am going to fast-track attendees to be empowered to start their company’s journey in Artificial Intelligence and related technologies such as Power BI. The conference covers everything from ingesting data to DevOps, so there is something for everyone and every business to be successful.

Register here before it is too late!

4_dps2019_Speakers

Marketing as a strategic business partner: mixing theory, research and #data

Marketing is viewed as a key strategic participant in achieving the goals of businesses, both large and small. I thought I’d share how we started to apply marketing theory, practice, and insights from data. There are tons of ‘skate on the surface’ marketing soundbite books, which may sound good at the first glance with no depth. But it’s quite a different thing to work at it so you really know it, practice it, and can share it with other people so it is authentic. It’s about focusing on the real, not the soundbite. Real never goes out of fashion.

Research evidence has shown that consumers interact with advertising in complex ways, especially since we have such short attention spans (Weilbacher, 2003). How do organizations know which way their decisions should land? After all, the Internet can break a business very quickly! So, organizations need a cohesive strategy which aligns all methods of communication to ensure consistency (Porter, 2001).

The overall company vision can be conveyed through the brand, which is shorthand for the company vision. The brand to communicate and distinguish an organization from other organizations. Brands can be viewed as a collective of perceptions that exist in the mind of the consumer, and the process of distinguishing and identifying products is known as branding (Doyle, 1999; cited in Baker, 1999).  Even in the industrial sector, where decisions are made by technical team members, brands can acquire confidence through familiarity (Levitt and Levitt, 1986).

Building a brand involves four key concepts: quality, service, innovation and differentiation (Doyle, 1999). Brands can also migrate from mature technologies to encompass new technology (Doyle, 1989). For example, over at Data Relish, we mix mature Business Intelligence technologies with new and upcoming technology, Artificial Intelligence is enjoying rapid growth (Xiong, 2019).

How can you tell what’s working for your organization? Particularly in the current economic climate and Brexit-dominated climate, it is important to identify the most targeted opportunities to maximise growth opportunities. It’s also important to collect customer feedback and references, which can be incredibly enlightening and occasionally even heartwarming! So what’s the secret?

  • The secret is the data. For example, at Data Relish, we have conducted some analyses in order to look at targeting and positioning better. It’s like having an arrow, and the segmentation is equivalent to pulling the arrow back so it gives you more power to go further.
  • It’s great to be insight-driven, but it’s also good to be data-driven and evaluate what the data actually says. It’s a balance.
  • Also, have good tools.

To do this, we used Power BI to examine the data from source systems such as HubSpot, FreeAgent and Insightly to understand better what was working from the marketing perspective. We also send the results to an independent marketing consultant for the purposes of replication and verification.

Obviously I’m not sharing the findings here, but it is safe to say that the results were enlightening. The UK Government estimates that AI could add an additional USD $814 billion (£630bn) to the UK economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9% (UK Government, 2017). By asserting experience as well as expertise, one thing that was working was that we were viewed as ‘trusted advisors’ in AI, helping to lead organizations at success by working with the C-suite to make the data work, and work hard to add business value. It became clear that we were perceived as a proven safe trusted advisor with experience in what’s perceived as a ‘young’ field, and this is a key differentiator which is proving invaluable in gaining clients in a variety of sectors. It seems that everyone is an expert in AI these days! But we could actually show it.

So, we made efforts to have real chops and apply our research and findings to our own organization, and to really analyze the data, and to apply marketing theory and practice to improving , informed by the academic literature. Data can help us to understand the research to explore and elicit reasons for avoidance of brands (Lee et al., 2009).

I’ll blog more on the journey with marketing with Power BI, HubSpot, FreeAgent and so on, but the main takeaway here is that you always learn something by having a fresh look at the data. Additionally, it’s important to get the results independently verified. If you need a hand with these concepts, don’t hesitate to get in touch over at Data Relish.

References

Doyle, P. 1999, ‘Branding’ in Baker, M (ed.) The Marketing Book.; 4th ed, Chartered Institute of Marketing., Oxford, UK.

Doyle, P. 1989, Building successful brands: The strategic options. Journal of Marketing Management, vol. 5, no. 1, pp. 77-95.

Levitt, T. & Levitt, I.M. 1986, Marketing Imagination. Simon and Schuster.

Porter, M.E. 2001, Strategy and the Internet. Harvard business Review, (2001): 63-78.

Porter, M.E., 2008. The five competitive forces that shape strategy. Harvard business Review, 86(1), pp.25-40.

Weilbacher, W.M. 2003, “How Advertising Affects Consumers”, Journal of Advertising Research, vol. 43, no. 2, pp. 230-234.

UK Government 2017,  Executive Summary. Available: https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk/executive-summary

Xiong, X. 2019, “Analysis of the Status Quo of Artificial Intelligence and Its Countermeasures”, 2018 International Workshop on Education Reform and Social Sciences (ERSS 2018)Atlantis Press .

Power BI Dataflows and fixing ‘Your Azure storage account must be in the same Azure Active Directory tenant as your Power BI tenant.’

I’m excited about Power BI dataflows, since I believe it’s a great way forward for companies to sort out the issue that nobody likes to talk about: cleaning data. I’m passionate about this topic since I believe in inheriting technical debt as much as I can, so I leave the organization’s technical debt in a better place than I found it. I’m taking the long view when I de

I tried to connect my Azure Data Lake gen 2 storage to Power BI, and I ran into this error message:

”Your Azure storage account must be in the same Azure Active Directory tenant as your Power BI tenant.’

I checked, and my Azure storage account was definitely in the same Azure Active Directory tenant as Power BI. So I was confused. What a pesky error! So I decided to investigate.

It turns out that I had missed a step when setting up the connectivity between Azure Data Lake gen 2 storage account and Power BI. I had missed assigning the Reader role to the Power BI service on the storage account.

Microsoft’s instructions are here and make sure you follow them to the letter. I missed a step because I rushed through it, and then spent time trying to figure it out, so it was my fault since I scrolled past a step. I’m recording this issue here in case anyone else gets this error message, and wonders what’s going on.

 

Connecting #Azure WordPress, #HubSpot data for analyzing data in #PowerBI for a small business #CRM

I got to the end of the free WordPress account for my small business account and I wanted to analyse my CRM and sales data better. I wanted to dial up my sales and marketing, and, of course, use data to understand my audience better. With the free WordPress edition, I could not do some of the things that I wanted, such as HubSpot integration and advanced analytics.

Why CRM?

As a small business, I rely on a lot of word of mouth business. When business leads come in, I need to track them properly. I have not always been very good at following-up in the past, and I am learning to get better at actioning and following-up.

 

I love the HubSpot CRM solution, and I decided I’d take it a step further by integrating HubSpot with my WordPress website, which is hosted in Azure and you can see my Data Relish company site here, with the final result. HubSpot have got great help files here, and I am referring you to them.

What technology did I use?

Microsoft Azure WordPress  – Azure met my needs since it could give me the opportunities for integration, plus additional space for storing resources such as downloads or videos.

Power BI – great way to create dashboards

HubSpot – CRM marketing and sales for small business

I found that using Microsoft Azure was a great way to make the jump from free WordPress to a hosted solution. Now, I am not a web developer and I do not intend to become one. However, I do want to use technology to meet my small business needs, and to do so in a way that is secure. I’m going to write up some posts on how to get started.

To get started with a website in Azure, you can follow the instructions here or watch the Channel 9 video for instructions.

Now, I needed a way of working with the HubSpot data in Power BI, and this is where the CData PowerBI and HubSpot connector comes in.
In running a small business, you need to be super-precious with your time. I could spend ages trying to create my own connector, or I could use a robust, off-the-counter connector that would do it for me.

In a small business, spending your time badly is still a cost.

In a business, you have to decide between spending money or spending time on an activity. If something is taking too long to do by yourself and someone/something could do it better but you have to pay for it, then it’s a false economy and a bad decision to do it by yourself. You’ve got a choice between expending time and effort, or a choice between spending money. Experience will tell you when to do what, but wasting time is difficult to measure.

There aren’t many options for Power BI and HubSpot, but I was pleased to find the CData connector.

Disclaimer: I didn’t tell HubSpot or CData that I was writing this blog so it isn’t endorsed by either of them.

What does CData look like?

You can download the CData ODBC Driver, which connects Power BI to HubSpot. Here’s a snip of their site:

CData PowerBI ODBC Driver

I downloaded the trial, and then went through the install. It was easy and ‘next next next’. When it is installed, it launches a browser to ask you to log into HubSpot, which I did. Then, quickly, I got the following screen – yay, I am in business!

CData Authorization Successful

Then, off to Power BI to download the latest edition of Power BI Desktop. It’s easy to install, and I could get cracking very quickly.

How do we get access to the HubSpot data?

In Power BI Desktop, click on the Get Data icon in the Home tab, and then choose the ODBC option.

Get Data ODBC

Click on the Connect button

Look for the HubSpot ODBC connector in the drop-down list. It should appear something like this:

ODBC Hubspot Power BI

Then, you will be asked for your name and password, and then click Connect:

ODBC HubSpot Username password

Once you have connected, you will be presented with a list of HubSpot tables

Hubspot Tables

Click the tables that you want, and the data will be loaded into Power BI.

If you don’t know which table you want, load in the tables starting with Deals first, then then compare it with the HubSpot screen. This will help you to understand better how the columns relate to your HubSpot data on your screen.

I’ll add more about HubSpot analysis in the future, but for now, happy PowerBI-ing!

Where do I find my #PowerBI tenant?

I’m writing this post for me, more than anyone else. I keep forgetting!

Log into your Power BI tenant and click on the Question Mark at the top right hand side.

Look for the option About Power BI. Here is an illustration below:

Power BI Images Find Tenant

 

Choose the option About Power BI and you will get a window appear. It’s the last item that shows what you need:

 Power BI Images Find Tenant//embedr.flickr.com/assets/client-code.js

So my tenant is located in North Europe.

I usually end up going to look for it in Tenant Settings but I suppose it isn’t there because you can’t change it. So I got fed up with myself forgetting it and wrote this post. If this is you too, I hear you.

Be Insights Driven! Why we should not just be Data Driven, and definitely not Tool Driven. #PowerBI, #Tableau, #Qlik

I presented at a ‘BI without the BS’ event in London in November 2018. The idea was that there would be three tools and three players; Qlik, Tableau and Power BI, a judging panel, and a live audience of about 100 people. I represented Power BI and it was very clear that only a few people had seen it before.

As a data visualization person, I’m all about making things clear for people and that was why my story was much simpler and the facts were compelling, displayed in Power BI; I told a story which had a call to action.

My interpretation of the rules

My interpretation of the rules was that data storytelling was one of the main judging criteria. I had not seen the event as a product demo, and that’s why I emphasized the insights that Power BI gave me over knobs and levers that each tool can do. Product demos are easy, but not everyone can find insights in data.

Google Chief Economist Hal Varian predicted that data storytellers are the future over a decade ago. With data as the new oil, every company is seeking new ways to monetize their data. So my emphasis had been on data storytelling with a particular tool, but not trying to sell the tool itself as a demo. That’s my boxing ring to play in, and that’s the hard part of working with data.

An Equal Playing Ground for Games?

For my Power BI piece, my insights were all about the complexities in being a woman in gaming. My Power BI analysis showed that women earned just $1.8m in prize money in total, but the top male game winners earned $145m in total. See the tiny pink sliver? That’s the proportion that women earn in gaming competitions. If you’re thinking, that’s not a good dataviz to show, I can barely see the sliver – then you have missed the point. The point is, it is a tiny sliver. For colours, I have used Dark Orchid to represent female players, and Teal to represent male data throughout the visualization.

Female vs Male Prize Money in Games

So there was a huge difference. In case you need the numbers:

card

So there is a huge disparity between male and female earnings. Is this because there are less women players? Apparently not. This chart shows that the gap between male and female players is narrowing:

Male vs Female Game Players by trend
In the data, I noted that the players were ranked from 1 to 100, for each gender. So I pitted the men and women together in terms of rank to show how the first ranked male player earned versus the first ranked female player. Here is the chart, in Power BI:

Prize Money by Gender

You can see that the girls’ earnings are practically flat, whereas the male earnings are vastly higher. The gender gap in pay is real, absolutely real.

I also noted that the lowest ranked player still earned more than double what the highest earning female player did. So the data showed lots of insights; it depended if you were willing to see things that made you uncomfortable.

Then, I wondered if there was a relationship between the number of tournaments played, and the prize money won. So, I used the male data here to see if  a reasonable relationship could be inferred between the number of tournaments played, and the amount of prize money earned. If women are playing less tournaments, then naturally they will earn less. So how did that pan out?

Relationship between No of Tournaments and Game Prize Money

The interesting thing was that the total number of tournaments played (on the X axis) didn’t seem to impact the amount of prize money earned. I’d have to do more analysis but you can see a vague relationship in the hexbin chart, but with a lot of outliers. I might come back and look at that another day, using R and Python or something.

Gaming in Real Life

So my piece was more about exploring the idea that there is no equal playing ground for women in gaming, and that’s certainly borne out by some of women’s experiences in the gaming world. Harassment for women in gaming can involve sexist insults or comments, death or rape threats, demanding sexual favors in exchange for virtual or real money, or even stalking. The GamerGate scandal tells you all you need to know about it, I suppose. Alternatively, you could look at Fat, Ugly or Slutty where women record instances of instances of sexism. Warning; it is not a pretty read. Or women hiding their identity online as a female, which is a safety measure that many women take. The most recent threat against female gamer Anita Sarkeesian was in Logan, Utah on October 15, 2014. She was scheduled to deliver a speech on a Wednesday evening until an anonymous email message arrived a day before, stating that there would be the deadliest school shooting in American history if the event was held. So don’t kid yourself that this isn’t real, and the impact means that many women are excluded from feeling that they can enter competitions in gaming.

Be Insights Driven, not Data Driven, and definitely not Tool Driven

When you analyze data, you bring your own personality and insights to analyzing data. I don’t believe that tools can solve problems; I believe that we have a lot of data, but no insight, information or wisdom unless we do something with the data. I don’t like the phrase data-driven; I prefer insights-driven. Qlik, Tableau or Power BI aren’t going to solve problems for you; they will just display data that hopefully brings about insights. The insights are yours and you can use each tool equally badly if you don’t have a story or a thread, or the data isn’t provoking an insight. We were all given the same data but we got very different results. That wasn’t down to the tool; it was down to the person driving the tool.

What I thought of the Tableau piece

I liked what Chris Love ( LinkedIn ¦ Twitter ) did; he clearly knows his stuff and it was nice to meet him in person. Funnily enough, we used to have the same boss when I had a boss (hello Tom Brown!).

I did find Chris Love’s visualization more interesting because he honed in on the journey of one player from the starting point to his success in winning a lot of money, and the journey was well displayed in Tableau. Chris had a good balance of context and detail, and for me, this was the data story telling piece. Here is the image below, credit to Laura Sandford:

 ChrisLoveTableau.jpg_large

What I thought of the Qlik piece

Nick Blewden ( LinkedIn, not on Twitter) is obviously fantastic at Qlikview and he did a good job of showcasing it. To be honest, I felt out of my depth here since it was a whizz tour of the product but since I’m not familiar with Qlik, I felt a bit bludgeoned with chart after chart and I couldn’t see a clear thread; it was information load as I tried to pick up the Qlik lingo as well as follow the story. I understand that Nick’s segment wasn’t aimed at beginners in Qlik and that’s ok with me; he only had five minutes to showcase what he’d done and he did a great job.

I am not familiar with the Qlik product set but a lot of the audience clearly were, and I could hear lots of mutters about ‘good to see he’s showing that feature’. So my perspective here is that of someone who does not know Qlik but who has expertise in Tableau and Power BI.  I can look at Qlik another time, if I choose.

I felt I’d let Power BI down at that point because I had not gone down that route of doing a product demo and I feel really bad about that. I had gone for the analytics and insights part because I’d understood the rules that way, and the audience can see a Power BI demo anytime they like.

One reason for me to present at the event was that I’d seen it as an opportunity to learn more about Qlik from the session, but all I saw was chart after chart. For me, there were lots of business intelligence dashboard and that’s fine and I think that it was a good product demo.

So my lasting takeaway from the Qlik segment is this dashboard was interesting because it showed that it was quick to produce a lot of charts very quickly, but sometimes ‘less is more’. I’m a fan of Stephen Few and he talks about the importance of finding the signal in the noise, and having a ton of charts can simply mean more noise if they are not meaningful. Here is the image below, credit to Laura Sandford:

NickBlewdenQlik.jpg_large

What I’d like to see next

I think I’d have preferred a larger, more mixed audience. A lot of people seemed to know one another already and I only knew one person in the audience. I’m not part of that community and it was nice to meet new people at the end.

Honestly, I’m not a fan of being shouted at by men I don’t know; it is really unpleasant. I think that the audience members should have the courtesy to refrain from shouting out during the performance. I was really put off with people shouting ‘Come on Qlik!’ and ‘Come on Tableau’ during the event. I didn’t hear a single voice for Power BI, not that it mattered; it really disrupted my thought flows to have people shouting when you’re trying to analyse data and I found it unsettling. Being at a live event isn’t like Gogglebox where the presenters can’t hear you.

So what did I think about the Power BI vs Tableau vs Qlik debate?

So what were my takeaways?

My call to action: be Insights Driven, not Data Driven, and definitely not Tool Driven.

According to the Qlik website ‘Deliver automated insight suggestions that help users see their data in new ways, auto-generating and prioritizing analytics and insights based on the overall data set and a user’s search criteria.’ Demystifying the marketing, it seems as if this means producing a ton of charts really quickly and if that’s what you’re looking for, it certainly did that. My overriding thought was that it can produce lots of charts but I really want to find meaning in charts, and I don’t measure meaning in charts by having as many charts as possible. I just got lost and I actually don’t think that’s good for Qlik.

For Tableau, I’d like to see Tableau become a real enterprise tool and it still feels like a cog in an enterprise wheel to me. I would not do any data prep in Tableau Prep although I do have experience in it; I’d want to use Power BI dataflows to clean data so that the data and the dataflows become part of the enterprise ecosystem.

I build big systems and I need to think big. When I’ve been working for customers, I’ve found it is easier to show ROI with Tableau and Power BI but it has taken longer for people to realize ROI with Qlik.

I am eternally confused by licensing and I find Tableau’s licensing simpler; Power BI and Qlik seem to be way more confusing to me. For Power BI, I always refer the customer back to their Microsoft reseller because they can figure it out for them.

My Power BI dashboard is here, for those of you who want to play with it:

https://app.powerbi.com/view?r=eyJrIjoiOTNmMzUyODAtZGFjZC00OTUxLWIxMmQtMDYzMTA5OWU1OGRkIiwidCI6ImFmMTA4OTMyLTkxNmQtNGUwNi1hZjVmLTAyMzg0NjZiZWRiMCIsImMiOjh9

Call to Action

I have put links, credits and sources here in case you want to play with the data.

Power BI Functionality

Colour Palette Used

Teal
#066082
#068
teal
hsl(196,91,26)
rgb(6,96,130)
Orchid
#b12acf
#b3d
darkorchid
hsl(289,66,48)
rgb(177,42,207)
Sandybrown
#fed044
#fd4
sandybrown
hsl(45,98,63)
rgb(254,208,68)

References

Distribution of computer and video gamers in the United States from 2006 to 2018, by gender. Source: Statista 
Why aren’t there more women in eSports?

 

Financial Storytelling and Data Storytelling with Profitability Ratios

Following on from my earlier blog post, discussing Accounting Ratios, let’s take a look at a specific set: Profitability Ratios.Profitability Ratios can be defined as understanding the effectiveness of the company in generating profit. In this blog post, we will concentrate on profitability ratios, working our way around the flow chart presented here:

Screenshot (1)

The formula are presented below, in terms of their meaning:

  • Return on Net Assets (RONA) (Return on Capital employed) – return on the fixed and current assets less current liability. It is also known as the primary ratio.
  • Return on Equity (Return on Shareholders’ Funds) – This also looks at return on the fixed and current assets less current liability, but it looks at it from the Shareholder’s perspective.
  • Gross Profit Margin – percentage of sales revenue remaining after the expense of making the product / solution / delivering the service is taken into account.
  • Net Profit Margin – This is the percentage of sales revenue that’s left, after all of the expenses of running the firm have been fully met.

Let’s have a look at how they are calculated, and what they mean.
money-2696229_1920

Calculating the Profitability Ratios

1.Return on Net Assets (RONA) (Return on Capital employed)

Net Profit before long-term interest and tax /

Total Assets less creditors falling due within one year

This figure is expressed as a percentage, so it is multiplied by 100%. That said, at root, it is fundamentally calculated as follows, expressed as a percentage:

Profit /

Capital

Profit can include one of the following metrics:

  • Operating Profit
  • Net Profit before Interest and Taxation
  • Net profit after taxation
  • Net Profit after taxation
  • Net Profit after taxation and preference dividend

Capital can be measured by one of the following options:

  • Total Assets
  • Total Assets less intangible assets
  • Total Assets less current liabilities
  • Shareholders Funds
  • Shareholders’ Funds less preference shares
  • Shareholders’ Funds plus long-term loans
  • Shareholders’ funds plus total liabilities

What combination do you choose? Basically, the MVP answer holds here: it depends. The definitions depend on what business question you are trying to answer. The RONA definition was chosen here in order to illustrate how it differs from Return on Equity, which is explained below.

2. Return on Equity (Return on Shareholders’ Funds)

Net Profit before long-term interest and tax /

Share Capital and Reserves

This figure is expressed as a percentage, so it is multiplied by 100%.

3. Gross Profit Margin

Gross Profit /

Sales

This figure is expressed as a percentage, so it is multiplied by 100%.

4. Net Profit Margin

Net Profit before long-term interest and tax /

Sales

This figure is expressed as a percentage, so it is multiplied by 100%.

It’s possible to see that these ratios are made of five different things. I’m a visual person so I’m marking these in colour since I will need to remember them for my exam!

  • Gross Profit
  • Net Profit before long-term interest and tax 
  • Sales
  • Share Capital and Reserves
  • Total Assets less creditors falling due within one year

Interpreting the Profitability Ratios

With the Return on Net Assets figure, we are looking at the effectiveness of the assets that are financed by long-term creditors as well as the shareholders, and we are looking at the profit generated as a result of these combined assets. Higher RONA can mean that the company using its assets efficiently. Also an increasing RONA may indicate an emphasis on executing efficiently, as evidenced in improved profitability and overall performance.

The Return on Equity ratio looks at the same issue, but from the perspective of the shareholder only. The long-term creditors are partialled out. Understandably, shareholders want to see a high return on equity ratio, since this would show that the organisation is being effective in its deployment of investors’ funds. Shareholders can also track progress by calculating the return on equity at the beginning of a period and then check it again at the end of a period to see if there is a change in return.

Net Profit Margin shows the sales revenue after all of the expenses have been removed. It should be as large as it can possibly be, as long as it is sustainable. However, this should not be taking place at the expense of another aspect of the business. Be wary of short-term attitude to profit. This is sometimes evidenced in the net profit margin.

Limitations on the Productivity Financial Ratios

Accounting Ratios give us useful insights in the management of a company, but it is not the whole story. The business context, and the company itself, should also be considered. RONA does not calculate a company’s future ability to create value. Additionally, the values on the balance sheet might not represent the replacement cost, therefore masking the reality of asset utilisation.

In the next post, we will look at Activity Ratios and how they are calculated, along with some advantages and limitations. We are leading up towards visualizing these ratios in Power BI, and it’s important to understand the ‘why’ as well as the ‘how’ of visualizing these ratios.