In the previous post I mentioned a few consolidated and unexpected (to me at least) areas in the energy sector where data play an important role.
However, it is important to notice that we have just begun and there are many hurdles to be overcome as shown in an Accenture report.
There are plenty of data but not all of them are available to all parties in the energy value chain, they may not be in synch one with the other and part of them can be just useless whilst others still need to be ... created.
Part of these problems stems from the fact that data have been "produced" to satisfy specific needs, measuring production, monitoring a machine and so on, and not as an overlaid digital industry and sector. Having data available has clearly prompted each player to take advantage of data analytics to get insight but usually these insight are not feeding back, at least not immediately to the industrial processes. They appear to be more like an after-thought.
Of course this is not solely happening in the energy sector, it is typical of all sectors and tied to the early stages of the Data Economy. Moving the operation of the whole life cycle from the atoms to the bits takes time and it is difficult because, as noted in the first series of these posts (from atoms to bits), it involves a change in the value chain with new players stepping in and "old" ones fighting for their survival as part of the value shifts to the bits.
According to Accenture, see figure, there is a need to integrate the Information Technology with the Operation Technology so that data generated by machines (atoms) are processed (bits) and feed back in real time to the machine (atoms) thus using the machine as sensors and actuators of policies and decisions taken at the bit level. The Data Economy, in this view, takes the upper hand.
Notice that Data are retrieved and have impact across several ownership domains, thus affecting the whole value chain. This is the big difference from the past (current) approach where there are silos and contractual exchange of values from one player to the next in the value chain.
In order to support the pervasiveness of data and their usage across multiple ownership domains, data and processes architectures need to change. In principle we have the technical tools for managing data across different domains by different players (the Cloud) but the outcome is disruptive and this is the big issues facing current players. Potentially they know that sharing data would increase effectiveness throughout the value chain but this leads to a loss of control and opens up to competition on a different plane with new rules of the game.
Once data are in the Cloud and there is an open data framework third parties may be enabled in offering service. and data analytics may be offered as service on demand.
This creates new biz opportunities and may even empower the end user to shift from one energy provider to another based on the intelligence provided by data analytics services. Clearly this would bring a further disruption in the market.
The availability of ubiquitous access, as provided by mobile connectivity, is a stronger enabler in the dissemination of data analytics to everybody starting of course with engineers in the field. Notably, the increased storage capacity at the edges, and in access devices (as well as in machines in the field) may be pushing part of the analytics at the edges, at the data usage point. I mentioned in the previous post the huge amount of data generated by turbines sensors, in the order of TB per day, and it can make sense to process -or preprocess- part of these data locally to decrease the toll on the communications networks, that particularly in remote areas where often production takes place may not be that effective.
In looking at Smart Grids and at IoT and Cloud EIT Digital is set to play a significant role in this area, particularly as it is focussing on leveraging the Data Economy.