Wednesday 11 February 2015

Information Management for Energy Suppliers - part 1


Unless you’re Google or IBM then Information Management is not your core business, which relegates it to a supporting function like HR or Accounting, and so Information Management only makes sense if it supports your core business and most importantly if the benefits actually exceed the costs. When I advise clients, I spend time first understanding their business model before prescribing an Information Management Strategy.

In the first of this two part blog, I am going to give a practical example of this by looking at the business of retail energy supply. Retail energy supply is characterised by high volumes, low margins, low customer loyalty and intense competition. These factors shape the business, so the logic is valid for other businesses with similar characteristics, like retail insurance. In my next blog, I will present an IM Strategy for such a business model.


A simplified business model for energy supply 

First of all, let’s look at the financial model. A UK energy supplier will buy gas and electricity on the wholesale market and look to re-sell it to retail customers with a target gross margin of say, 23%. On top of the energy, the customer also pays transport and distribution fees for the delivery of the energy to their home as well as VAT and other taxes. The energy supplier collects these fees and passes them on to the appropriate companies and the treasury without adding any mark-up. In theory these charges are a zero-sum game; the supplier collects the money from the end customer and passes them on to the third party. In practice, zero is the best that the supplier can ever hope to achieve as we shall see.

Where the money goes

The supplier also has operating costs to consider. Because the business model is simple, and many processes can be fully automated, most of these costs are fixed costs independent of the size of the business, and a large part of the fixed costs is IT. Variable costs include the cost of sending bills and collecting payments and the cost of running a call centre to handle changes, questions and complaints.

As you can see from the above breakdown, above a 23% gross margin on traded energy quickly ends up becoming a 2% net profit for electricity and 7% for gas. A quick check on a comparison website will show that the differences between the suppliers are more than their net profits. This indicates how competitive the market is; the cheapest suppliers are offering prices that are below the costs of their more expensive competitors. How do they do this?

There are few options available to the energy supplier. The obvious ones are reducing the only input costs over which the supplier has control, namely the wholesale price and the operating costs.
In the wholesale market volume, accuracy and a good hedging strategy are the best way to get the best price. Accuracy is important because energy is ordered in advance on an hourly basis. The volumes ordered are passed on to power stations and gas shippers who will ensure that it is available on the grid. The price paid depends on the energy ordered and the energy actually consumed by your customers, and there are two components. The contracted price is for the ordered volume and the balance price that is paid for the difference between ordered and consumed volumes – it is effectively a penalty for ordering too much or too little energy. To get the best price, you need to minimise the imbalance price. You also get a better price if you order very high volumes through having a large market share.

For operating costs, volume is important again since the fixed costs are so high, but so is customer satisfaction, since happy customers don’t need to call the call centre. Next to that big savings can be made by sending bills electronically and by serving your customers with a good website. Intuitively it would seem that one way of increasing volume and keeping customers happy is to reduce your selling price. After all, rational customers will choose the supplier with the lowest price, and knowing that they are paying the lowest price will make them happy. In practice it’s not that simple. Customers expect predictability and customer service just as much as they expect a fair price. There is a saying in the market that "customers come for the price, but they stay for the service." Replacing lost customers with new customers is very expensive; it costs less to keep the customers you have happy. Getting the balance right enables you to offer a competitive price, retain customers and still make a small margin.

Happy customers are loyal customers
However, there is bigger problem that needs to be addressed – unpaid consumption. When consumption is not paid for, the supplier is still liable for the wholesale energy costs, transport and distribution costs and the taxes. When net margins are so slim, they are quickly eroded unpaid consumption. For every £100 of electricity that is not paid for, the supplier needs to bill and collect £5,000 of electricity consumption just to recover the losses.
So optimising the business of energy supply means:
  • Ensuring that the energy that you supply is paid for
  • Keeping operating costs low by having a large market share and happy customers
  • Keeping wholesale energy costs low by having a large market share and accurate forecasts

I’ll come back to those aspects and how Information Management can help them in my next blog.

Tuesday 6 January 2015

New Year's Resolution for Data Management : Stop Using Analogies

I recently found myself in a surreal discussion in which it was suggested to me that meta-data was like poetry. I was asked to consider the line "I wandered lonely as a cloud" and it was suggested to me that "I" am the data, "lonely" is meta-data, and "as a cloud" is meta-meta-data. My interlocutor beamed at me with pride, and waited for my confirmation of their brilliance.

It's poetry, not meta-data

My heart sank as I realised that analogies are really not very do not helpful. Like many in Information Management, I have been guilty of using them far too much. I have now resolved to stop using analogies, and instead to make the effort to understand how others see the world, and to explain Information Management in terms that they understand about data that they really use.

Why do we use analogies?

A lot of the material in Information Management is pretty abstract, and it only really makes sense once you have already done it a couple of times. Anyone working in Information Management will be familiar with the challenge of explaining what it is and why you should do it. Meta-data is a great example, and the simple definition of "data about data" doesn't really help anyone new to the topic. So there's a temptation to introduce analogies. As well as poetry, a recent engagement threw up analogies including finger prints, traffic rules and photography. We use them to try and explain concepts that we understand to someone who doesn't.

Why analogies don't help

There are two problems with analogies, though. The first is that they eventually break down. Once you have introduced an analogy, the discussion inevitably explores it further and you end up debating where it is valid and where not. The second problem is that an analogy only really works for the person who came up with it. One of my favourites is weeding a garden as an analogy for data quality. To me it makes sense, because the weeds will always come back, whatever I do. So although I can aim for a weed free garden, I know that I will never achieve it. It's the same with Data Quality, while you may aim for zero defects, you can never actually achieve it. For those who don't garden, it doesn't help. For those who do garden, the discussion moves on quickly to dandelions, moss and bindweed. Either way, we're not getting very far with Data Quality, and the analogy breaks down because no one ever creates a weeding dashboard or assigns gardening stewards.

It's weeding, not Data Cleansing


From the specific to the general and back again

One of the problems comes from the way that we in Information Management think. As a group we tend to look for patterns and we are constantly seeking general abstractions in a sea of specific examples. A good example is the party data model which was born from the observation that customers, suppliers, employees, representatives and so on have common attributes and that they can be generalised as persons or organisations, that they can be related and so on. There are other generalised data models that have been developed over years. It's what we do, we can't help it. That's why we ended up in Information Management in the first place.

The problem is that the rest of the world doesn't think like this. Most people consider customers and suppliers to be fundamentally different, and a party is an event where people celebrate. We need to get back to specifics that are relevant to our stakeholders and give them concrete examples of what we are talking about.

My kind of party


From poetry to SOAP

In order to move the conversation away from poetry and onto something more useful, I dug a little deeper to find something more specific and relevant to someone seeking to understand meta-data. The guy that I was talking to had experience of integrating systems, so we talked about exchanging data between two or more systems. He suggested SOAP (www.w3.org/TR/soap12-part1) as his preferred protocol. Then we discussed how a SOAP message is specified as an XML Information Set, and that this is an example of meta-data. As we were on familiar ground, I could explain to him why such an Information Set would need to be owned, why it should be approved, why changes should be carefully managed, and what the risks of an incomplete definition would be. From this example, which he understood, the definition of "data about data" made sense, the need for formally managing it made sense, and he could begin to understand that these principles would apply in other scenarios.

And finally

If you must use poetry as an analogy for meta-data, then I suggest commentary on poetry is better. Lewis Carrol's Jabberwocky is one of my favourite poems, the wikipedia entry for it is a commentary and so it is writing about writing (en.wikipedia.org/wiki/Jabberwocky).

Oh dear, I've just broken my New Year's Resolution already.