The possible future developments relating to RIMDB in this thesis can serve as guidance for RIMDB vendors, but can also be highly relevant for other parties involved in big data analysis, such as providers of alternative or complementary technologies or companies planning on implementing a big data analysis system.
All three scenarios foresee a strong development of the IoT, and in fact all of the 100 most consistent scenarios point in this direction. However, one cannot neglect the fact that data sources in the IoT will be multi-faceted and data will be of unstructured, semi-structured and structured nature. Since RIMDB are primarily suitable for structured data, it is thus important for RIMDB vendors to consider in how far they might be able to either partner with, provide gateways to or integrate systems for unstructured or semi-structured data. First examples going in this direction are Microsoft’s Polybase23, Oracle’s Big Data SQL24, and the integration of SAP HANA with the Hadoop framework25. There is room to further develop solutions in this direction.
In all scenarios, a well-developed network and corresponding connectivity are important as a highly relevant foundation in the big data environment. Looking at the scenarios, the question remains whether government efforts alone can reach sufficient connectivity, as is the case in scenario three, or whether businesses should support the development themselves, e.g. by investing into innovative
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For more information, see https://msdn.microsoft.com/en-us/library/mt143171.aspx 24
For more information, seehttp://www.oracle.com/us/products/database/big-data-sql/overview/index.html
connectivity solutions such as LiFi or developing their own network grids in areas with low connectivity. Because of bad connectivity, not even near real-time data transfer can be an option for many businesses. By supporting network development and broadening the areas where real-time data transfer is possible, e.g. by providing infrastructure of their own when necessary, RIMDB vendors could gain new customer groups. Otherwise, although in none of the 100 most consistent scenarios this was the case, the network and lack of sufficient connectivity could indeed become a bottleneck for the IoT development, which would mean less valuable data could be collected. This in turn could have negative effects for the diffusion of RIMDB (and other big data solutions).
For RIMDB users, and users of other real-time data analysis systems, the example of insurance providers described in scenario one is very relevant: Competitive advantage from implementation of real-time systems is likely going to be subject to considerable volatility and ephemerality. First movers implementing such systems and creating new business models, products and services may be able to capture considerable market share, but the question remains whether this will be enough to build a sustainable competitive advantage. Businesses should give serious thought to this aspect when working on real-time based business models, products and services. Also, RIMDB vendors (and other big data analysis providers) can also start to consider how they can continue adding value to users once real-time becomes the state of the art.
Along these lines, it may also be interesting for RIMDB vendors to think about the proposed platform business model of scenario three, which could ensure long-term recurring profits even when the market for RIMDB systems is already saturated, or open-source solutions steal a portion of the market share of commercial RIMDB vendors. Naturally, the story of Google and Oracle creating a largely popular open-source RIMDB based in the cloud of scenario one is only an example based on current developments and trends, and should serve to show that it is not impossible that proprietary RIMDB may lose their positioning. Such a platform business model, as proposed in scenario three, may therefore be more sustainable on the long run when competing with big data giants such as Google or Facebook. As Van Alstyne of the MIT Initiative on the Digital Economy put it, “Platforms beat products every time.” (Accenture, 2016, p.42). Overall, RIMDB vendors are advised to consider the possibility that open-source RIMDB –and for that matter, also other open-source big data analysis solutions– could play an increasingly relevant role and gain market share.
Lastly, in particular the first scenario shows that governments may also be a highly relevant customer group for big data analysis systems, including RIMDB. Partnering with governments may also be interesting when it comes to open data which could be pre-analysed or provided as add-on packages to RIMDB, as described previously.
A certain awareness may need to be raised in the more price-sensitive SMEs with low technological abilities about current digitisation and IoT developments, and about the dangers of falling behind from a competitive perspective. Here, the price point of RIMDBs may be highly relevant. Another important
aspect, which did not appear in the key drivers but is part of the 31 drivers is a lack of sufficient skills in many companies, especially SMEs to handle the new big data technologies. RIMDB vendors (and other providers in the field of big data) could support these companies, firstly in developing these necessary skills, and secondly by making their systems available at more affordable rates. On the long run, such efforts could open up a large new customer group for RIMDB vendors.
On top of that, RIMDB vendors could also consider adding further value for their customers by e.g. selling additional data packages. It may be particularly interesting for companies to mix (structured) transactional data with (unstructured) sensor data. One example case could be combining unstructured data from the sensors of a production robot with (structured) ERP planning numbers to gain further insight, and/or update both systems with real-time changes. This example already points to the next section on conclusions for Industry 4.0.