Uncover hidden opportunities with energy data analytics

What springs to mind when you hear the words energy data analytics? To me, I feel like energy data analytics is not my thing. Energy data analytics, however, is of great importance to any organisation or business that wants to run more efficiently, reduce costs, and increase productivity. Energy efficiency is one of the best ways to accomplish these goals.

Energy efficiency is not about investment in expensive equipment and internal reorganization. Enormous energy saving opportunities is hidden in already existing energy data. Given that nowadays, energy data can be recorded from almost any device, a lot of data is captured regularly and therefore a lot of data is readily available.

Organisations can use this data to convert their buildings’ operations from being a cost centre to a revenue centre through reduction of energy-related spending which has a significant impact on the profitability of many businesses. All this is possible through analysis and interpretation of data to predict future events with greater accuracy. Energy data analytics therefore is about using very detailed data for further analysis, and is as a consequence, a crucial aspect of any data-driven energy management plan.

The application of Data and IT could drive significant cost savings in company-owned buildings and vehicle fleets. Virtual energy audits can be performed by combining energy meter data with other basic data about a building e.g. location, to analyse and identify potential energy savings opportunities. Investment in energy dashboards can further enable companies to have an ongoing look at where energy is being consumed in their buildings, and thus predict ways to reduce usage, not to mention that energy data analytics unlock savings opportunities and help companies to understand their everyday practices and operating requirements in a much more comprehensive manner.

Using energy data analytics can enable an organisation to: determine discrepancies between baseline and actual energy data; benchmark and compare previous performance with actual energy usage. Energy data analytics also help businesses and organisations determine whether or not their Building Management System (BMS) is operating efficiently and hitting the targeted energy usage goals. They can then use this data to investigate areas for improvement or energy efficient upgrades. When energy data analytics are closely monitored, companies tend to operate more efficiently and with better control over relevant BMS data.

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Keys to Successful Matrix Management

Matrix management, in itself, is a breakthrough concept. In fact, there are a lot of organizations today that became successful when they implemented this management technique. However, there are also organizations that started it but failed. And eventually abandoned it in the end.

Looking at these scenarios, we can say that when you implement matrix management in your organisation, two things can happen – you either succeed or fail. And there?s nothing in between. The truth is, the effectiveness of matrix management lies in your hands and in your implementation. To ensure that you achieve your desired results, recognise these essential keys to successful matrix management.

Establish Performance Goals and Metrics

This should be done as soon as the team is formed, at the beginning of the year or during the process of setting organisational objectives. Whenever it is, the most important thing is that each team player understands the objectives and metrics to which their performances will be evaluated. This ensures that everyone is looking at the same set of objectives as they carry out their individual tasks.

Define Roles and Responsibilities

One pitfall of matrix management is its internal complexity. Awareness of this limitation teaches you to clearly define the roles and responsibilities of the team players up front. Basically, there are three principal sets of roles that should be explained vividly ? the matrix leader, matrix managers and the matrixed employees. It is important to discuss all the possible details on these roles, as well as their specific responsibilities, to keep track of each other?s participation in the projects of the organisation.

One effective tool to facilitate this discussion is through the RACI chart – Who is Responsible? Who is Accountable? Who should be Consulted? Who will Implement? With this, clarification of roles and responsibilities would be more efficient.

When roles are already clearly defined, each participant should review their job descriptions and key performance metrics. This is to make sure that the roles and responsibilities expected of you integrates consistently with your job in the organisation, as a whole.

Manage Deadlines

In matrix management, the employees report to several managers. They will likely have multiple deadlines to attend to and accomplish. There might even be conflicts from one deadline to another. Hence, each should learn how to schedule and prioritise their tasks. Time management and action programs should be incorporated to keep the grace under pressure.

Deliver Clear Communication

Another pitfall of matrix management is heightened conflict. To avoid unrealistic expectations, the matrix leaders and managers should communicate decisions and information clearly to their subordinates, vice versa. It would help if everyone will find time to meet regularly or send timely reports on progress.

Empower Diversity

Knowledge, working styles, opinions, skills and talents are diverse in a matrix organisation. Knowing this fact, each should understand, appreciate and empower the learning opportunities that this diversity presents. Trust is important. Respect to each other?s opinions is vital. And acknowledgement of differing viewpoints is crucial.

The impetus of matrix management is the same ? mobilise the organisation’s resources and skills to cope with the fast-paced changes in the environment. So, maximise the benefits of matrix management as you consider these essential keys to its successful implementation.

User-Friendly RASCI Accountability Matrices

Right now, you’re probably thinking that’s a statement of opposites. Something dreamed up by a consultant to impress, or just to fill a blog page. But wait. What if I taught you to create order in procedural chaos in five minutes flat? ?Would you be interested then?

The first step is to create a story line ?

Let’s imagine five friends decide to row a boat across a river to an island. Mary is in charge and responsible for steering in the right direction. John on the other hand is going to do the rowing, while Sue who once watched a rowing competition will be on hand to give advice. James will sit up front so he can tell Mary when they have arrived. Finally Kevin is going to have a snooze but wants James to wake him up just before they reach the island.

That’s kind of hard to follow, isn’t it ?

Let’s see if we can make some sense of it with a basic RASCI diagram ?

Responsibility Matrix: Rowing to the Island
Activity Responsible Accountable Supportive Consulted Informed
Person John Mary Sue James Kevin
Role Oarsman Captain Consultant Navigator Sleeper

?

Now let’s add a simple timeline ?

Responsibility Matrix: Rowing to the Island
? Sue John Mary James Kevin
Gives Direction ? ? A ? ?
Rows the Boat ? R ? ? ?
Provides Advice S ? ? ? ?
Announces Arrival ? ? A C ?
Surfaces From Sleep ? ? ? C I
Ties Boat to Tree ? ? A ? ?

?

Things are more complicated in reality ?

Quite correct. Although if I had jumped in at the detail end I might have lost you. Here?s a more serious example.

rasci

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There?s absolutely no necessity for you so examine the diagram in any detail, other to note the method is even more valuable in large, corporate environments. This one is actually a RACI diagram because there are no supportive roles (which is the way the system was originally configured).

Other varieties you may come across include PACSI (perform, accountable, control, suggest, inform), and RACI-VS that adds verifier and signatory to the original mix. There are several more you can look at Wikipedia if you like.

Directions Hadoop is Moving In

Hadoop is a data system so big it is like a virtual jumbo where your PC is a flea. One of the developers named it after his kid?s toy elephant so there is no complicated acronym to stumble over. The system is actually conceptually simple. It has loads of storage capacity and an unusual way of processing data. It does not wait for big files to report in to its software. Instead, it takes the processing system to the data.

The next question is what to do with Hadoop. Perhaps the question would be better expressed as, what can we do with a wonderful opportunity that we could not do before. Certainly, Hadoop is not for storing videos when your laptop starts complaining. The interfaces are clumsy and Hadoop belongs in the realm of large organisations that have the money. Here are two examples to illustrate the point.

Hadoop in Healthcare

In the U.S., healthcare generates more than 150 gigabytes of data annually. Within this data there are important clues that online training provider DeZyre believes could lead to these solutions:

  • Personalised cancer treatments that relate to how individual genomes cause the disease to mutate uniquely
  • Intelligent online analysis of life signs (blood pressure, heart beat, breathing) in remote children?s hospitals treating multiple victims of catastrophes
  • Mining of patient information from health records, financial status and payroll data to understand how these variables impact on patient health
  • Understanding trends in healthcare claims to empower hospitals and health insurers to increase their competitive advantages.
  • New ways to prevent health insurance fraud by correlating it with claims histories, attorney costs and call centre notes.

Hadoop in Retail

The retail industry also generates a vast amount of data, due to consumer volumes and multiple touch points in the delivery funnel. Skillspeed business trainers report the following emerging trends:

  • Tracing individual consumers along the marketing trail to determine individual patterns for different demographics and understand consumers better.
  • Obtaining access to aggregated consumer feedback regarding advertising campaigns, product launches, competitor tactics and so on.
  • Staying with individual consumers as they move through retail outlets and personalising their experience by delivering contextual messages.
  • Understanding the routes that virtual shoppers follow, and adding handy popups with useful hints and tips to encourage them on.
  • Detecting trends in consumer preferences in order to forecast next season sales and stock up or down accordingly.

Where to From Here?

Big data mining is akin to deep space research in that we are exploring fresh frontiers and discovering new worlds of information. The future is as broad as our imagination.?

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