Machine Learning as a Service ist zu einem umkämpften Markt geworden, in dem sich nahezu alle wichtigen IT-Provider positionieren. Sie müssen die Funktionen, zugrundeliegenden Algorithmen und Modelle dabei qualitativ wie quantitativ schnell weiterentwickeln, um Marktanteile und Kunden zu gewinnen. Dies stellt der neue große Anbietervergleich „ISG Provider Lens Germany 2019 – Data Analytics Services & Solutions“ fest. Die Studie verzeichnet bei Machine-Learning-Lösungen zudem eine deutlich gestiegene Portfolioattraktivität und Produktreife. Die Veränderungen am Markt sind dabei zunehmend exponentiell, weil zum Beispiel nun auch die großen Public Cloud-Provider auf dieses Thema setzen. Über den Markt für Machine Learning as a Service hinaus untersuchten wir in der Studie insgesamt rund 70 Anbieter in fünf Teilmärkten.
Microsoft zählt zu einem der systemrelevantesten IT-Provider – und dies weltweit. Im Verbund mit rund 30.000 Partnerunternehmen in Deutschland, davon 70 bis 100 unternehmenskritische, ca. 1.000 relevante und den restlichen als Long-Tail-Partner, adressieren die im Microsoft-Ökosystem aktiven Unternehmen Firmen aller Branchen und Größen.
Business-Intelligence – respektive Big-Data-/Analytics-Anbieter – analysieren eine Vielzahl von unstrukturierten Daten. Social-Analytics-, auch sogenannte Socialyticsanbieter, legen den Fokus dabei speziell auf die Datenanalyse von Individuen bzw. auch Unternehmen innerhalb von Social-Media-Plattformen.
Beim Markt für Engineering Service handelt es sich zwar um einen traditionellen Markt, derzeit ist aber ein hohes Maß an Veränderung zu erkennen. Die Digitalisierung hat in den letzten Jahren die Art und Weise, wie Engineering Services durchgeführt und erbracht werden, sowie das Thema selbst stark verändert. Einer der Hauptgründe ist, dass auch die Ergebnisse des Engineerings immer mehr digitale Elemente beinhalten.
Bottom Line (ICT-Anwenderunternehmen): Social Media haben in der Gesellschaft einen hohen Stellenwert. Auch Unternehmensentscheider und Einkäufer tummeln sich auf den unterschiedlichen Plattformen. Durch zielgerichtete Maßnahmen ist es möglich, diese im Entscheidungsprozess zu beeinflussen und auf die eigenen Produkte zu verweisen. Spezialisierte Agenturen und Tools helfen bei der Umsetzung. Bottom Line (ICT-Anbieterunternehmen): Viele B2B-Anbieter haben noch […]
What Is Happening?
The use of automation in IT, Finance, and HR operations is growing and accelerating. And so far, it is more likely to improve productivity by removing robotic tasks from humans than by replacing humans with robots – so far.
ISG released this week its latest installment of the ISG Automation Index™, an analysis report focused on the use and impact of automation in IT services contracts and business support functions. The research leverages data collected from recently signed ISG-advised ITO contracts with a significant automation component and ISG-advised robotic process automation (RPA) assessments in Finance, Accounting and Human Resources. The report provides the most current analysis of how automation is changing the nature of IT services and business support functions.
Key findings from the report include the following:
Service provider productivity is surging. Employee productivity is improving across all towers by 24 to 143 percent; this is in sharp contrast to a historical norm of 5 to 10 percent.
Figure 1: Average Service Provider Productivity Improvement by Tower. Source: ISG Insights.
Costs are declining, especially in areas where software is replacing hardware. Against ISG market benchmarks, double-digit cost reductions continue, with network and email management services showing the sharpest cost reductions, at 64 and 71 percent respectively.
Figure 2: Average Service Provider Cost Reduction by Tower. Source: ISG Insights.
Shared services processes using RPA require an average of 37 percent fewer resources. Procure-to-pay, order-to-cash, record-to-report and hire-to-retire processes, as well as a number of vertical-specific processes, such as loan servicing and underwriting, all require significantly fewer resources to execute with the application of RPA than those same processes without RPA.
Figure 3: Average FTE Reduction by Business Process after RPA. Source: ISG Insights.
Why is it Happening?
For IT service providers, competition is fierce. In one out of every two competitive renegotiations, providers lose all of the scope they once managed for the client. Offshore labor rates and ratios are in flux as well, making labor arbitrage a less effective/certain way to reduce prices for cost-conscious customers. And finally, adoption of Software-as-a-Service and Infrastructure-as-a-Service platforms in on the rise while traditional outsourcing is generally flat. These factors, combined with the need to decouple a business’ potential for growth from the number of people it employees, is driving IT service providers to aggressively incorporate automation into their service delivery model.
For business buyers, the rapid emergence of digital business means that customers, employees and partners need access to products and services in real-time. It also means that transaction volumes created by new digital experiences are increasing. The challenge for business support functions is that their budgets are flat to shrinking – even in the face of new digital requirements. Therefore, buyers are turning to technologies like RPA to execute business processes faster, improve quality and compliance and avoid future costs – usually in the form of hiring new people to handle increased volumes.
For IT service providers, the impact will be sudden and dramatic. While the ITO deals we analyzed for this report do not reflect the entire $114B outsourcing market, they do represent the new types of contracts we see emerging in our client activity. We believe that in the second half of 2017, and into 2018, the size and number of contracts with a significant automation component will grow quickly, which will put even greater pressure on service provider pricing. The question will be: can service providers deliver on productivity commitments? As discussed in the report, we believe providers are committing “ahead of the curve” in some cases, and they have not yet fully proven that their automation software can reach the committed levels of productivity.
Given the newness of the technology and the conflict that exists today between IT and business support functions in areas like security and compliance, business buyers will feel the impact more gradually. However, as successful RPA implementations continue and business benefits accrue, adoption will broaden and accelerate, encompassing even more business support functions. This will, in turn, have a profound impact on the business process outsourcing (BPO) market, as enterprise leaders begin to opt for delivery models that focus on a small number of in-country, high-skilled resources supported by a large number of robots over a traditional outsourcing delivery model that depends on a large number of offshore resources. Additionally, as RPA gets “smarter” with the help of machine learning algorithms, the kinds of business processes that can be automated will only increase.
In both scenarios, today we see “task” automation versus “role” automation. This means slices of jobs are being automated, not entire jobs. In most cases, we see two impacts of this: 1) humans workers simply take on more work with the assistance of a bot or 2) humans have more capacity to accomplish higher-value work. However, as more and more tasks are automated, it is only a matter of time before entire roles will be automated. The tipping point has not yet been reached, but, given how quickly these technologies are maturing, it is not likely far away.
What is Happening?
You would need to be hiding under a rock to not have noticed the recent rise in popularity of the term “machine learning.” This statistical method, which consists of using computers to build complex and sensitive predictive algorithms, underpins much of what today is being billed as artificial intelligence. While machine learning has been around in various forms for many decades, improved access to computational power in the cloud – as well as the application of machine learning capabilities by large internet companies such as Amazon, Google and Facebook to improve recommendations, searches and content filtering – have made it top-of-mind for businesses engaged in Digital Transformation. Depending on how it is applied, enterprises can use machine learning to improve targeting and interacting with customers, to better automate tedious tasks in back-office processes or to help model and avoid financial risk – a very flexible tool indeed.
Figure 1 – Google Trends Data – Machine Learning. Source: Google Trends (Accessed 20 April 2017). Note: Google Trends data is represented as Search Interest in the given term over Time, with 100 indicating peak popularity and 50 indicating the term was half as popular.
As seen in Figure 1, the growth in interest in this topic is clearly visible with the search instances roughly doubling over the last year.
Why is it Happening?
The fact that businesses, software engineers and statisticians are using a new method isn’t particularly noteworthy in and of itself, but, when taken in context, it represents the beginning of yet another important shift in the evolution of computing. Until now, the evolution has happened in three epochs, starting from early tabulating machines and progressing to the extensive mobile and social networks of today. The series of developments goes (roughly) like this:
- Systems of Record – developed primarily to store data about the business
- Systems of Interaction – developed to provide new ways to work with data in the Systems of Record and to focus on creating, reading, updating, deleting – or “CRUD” operations
- Systems of Engagement – developed to move beyond a strict transactional system to add share and react functions on top of CRUD operations. These systems formed the basis of collaboration and enabled a level of abstraction from the transaction to better allow users to understand the process and engage with it.
Each of these developments has been built upon the last, and none of them replaces the ones that came before it. Instead, they offered additional abstraction from the data and the computer’s processes to get closer to what the human processes needed to be. While the nature of these systems is not predicated on the evolution of databases, the concomitant evolution of databases from tabular > hierarchical > relational > graph and unstructured has also enabled many of the additional capabilities of these systems over time
As Systems of Engagement have matured over the last few years, we have been watching closely to see where the innovation was likely to lead next. Based on our recent survey and interview programs, we believe two more systems will be added to the three existing ones, and that they will be:
- Systems of Understanding
- Systems of Intelligence
The surge in interest in machine learning and the increasing maturity of predictive analytics technologies already are building this nascent group of systems to help us understand the world around us. “Cognitive” systems, as Systems of Understanding are frequently called, include many sophisticated tools that help expand the scope of both human and computer decision-making by giving us new ways of understanding data and the relationships therein. The computing systems being developed now build on top of Systems of Record, Systems of Interaction and Systems of Engagement to help us describe, correlate, and predict based on information to which we have access.
While Systems of Understanding are still immature, we are seeing early evidence of a future of Systems of Intelligence, which will be defined by the ability to understand within a real-world context. This capability will allow computers to navigate beyond describing correlation to inferring causation.
In den letzten Wochen haben wir uns im Rahmen einer Kolumne mit den Möglichkeiten der Analyse von Twitter zum Zwecke des Trend-Monitorings beschäftigt. In diesem abschließenden Teil werden die Erkenntnisse nochmals gebündelt erläutert und beschrieben, wie Unternehmen diese nutzen können. Twitter gilt als Kanal für Kommunikation in Echtzeit, was eine hohe Dynamik und gewisse Frühzeitigkeit […]
Nachdem in den ersten drei Teilen dieser Kolumne zum Einsatz von Twitter für das Trend-Monitoring die generelle Relevanz, inhaltliche Analysen und die Identifizierung von Influencern diskutiert wurden, geht es in diesem Teil darum, die Erkenntnisse, die Unternehmen aus der Analyse der Twitter-Daten ziehen können und müssen, zu erläutern. Analyse von Twitter allein reicht nicht aus Zunächst ist anzumerken, dass durch […]
Auf Twitter wird täglich eine Vielzahl von Daten generiert. In unserer Kolumne gehen wir darauf ein, wie diese Daten – sinnvoll aufbereitet – für das Trend-Monitoring genutzt werden können. Nachdem wir im ersten Teil die generelle Relevanz und anschließend geeignete inhaltliche Analysen der Tweets erläutert haben, ist es in einem weiteren Schritt nötig, Trendtreiber zu […]