While innovation can be categorized in familiar terms -- such as a new product, service, or business model -- we're finding companies are applying analytics and artificial intelligence techniques to the concept of innovation. Organizations we work with are turning to tools such as machine learning to pursue a fast, effective route to innovative results as they seek to stay competitive and top-of-mind for consumers.
For our Accenture Technology Vision 2016 report, we surveyed 3,100 business and IT executives in 11 countries. We found that 70% of respondents are making significantly more investments in artificial intelligence technologies than they did in 2013, with 55% stating that they plan on using machine learning and embedded AI solutions extensively.
An analytics-driven approach to innovation involves a blend of technological experimentation and cultural collaboration -- bringing together fresh minds and analytics techniques to uncover insights and ideas which can inspire agility, industry disruption and customer loyalty.
So, where does IT fit in?
IT professionals have several options to help their companies pursue a competitive edge through analytics-driven innovation. It all starts with culture, and culture typically starts at the top.
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If your organization currently lacks an adventurous analytics culture, you may find it challenging to move the needle from where you sit in the IT department. Here, then, are five actions you could encourage your company to take to help you and the larger workforce pursue an analytics-driven approach to innovation:
- Forming strong executive sponsorships to communicate and endorse analytics goals.
- Simplifying analytics for the user with intuitive analytics technology.
- Recognizing the fluidity of analytics projects and understand our operating models, processes and technologies will be continually adjusted to reach the goal.
- Actively educating business and IT teams on analytics tools and processes, and reinforcing the new learnings through individual KPIs.
- Encouraging the organization to make decisions based on data insights over gut feelings, therefore maximizing the value of analytics.
In addition to an analytics culture, it's also important to leverage the technologies which best support analytics experimentation. These are three options worth exploring for every IT professional.
No matter where on the analytics adoption spectrum your company resides -- it isn't quite ready for analytics yet, it’s experimenting with analytics, or it’s a complete insight-driven enterprise -- you'll do your long-term career a favor by learning more about these tools:
- Cloud-based analytics. An increasing number of our clients, including the largest provider of water and wastewater services in the UK and an oil company in Australia, are using cloud-based analytics platforms to experiment with their data. These platforms obtain analytics insights at a fast pace and don’t require a large upfront investment. They can typically generate initial actionable analytics insights within weeks, opposed to conventional approaches which can take months. For example, a global integrated oil company in Canada is using a cloud-based analytics solution to analyze its RFID data and data visualization tools to make informed decisions in order to help its workforce meet deadlines. For this organization, missing deadlines could result in $10 million worth of lost revenue for each day of delay.
- Data visualization and applications. Tools to make analytics accessible to more people, such as data visualization technologies and advanced analytics applications, provide analytics insights in visually appealing formats. This helps your business-side colleagues confidently make decisions. As the number of people in your organization who are involved and comfortable with data and analytics increases, you'll see a growth in the number of innovative data-driven ideas being generated, shared, and explored.
- Machine learning. Machine learning, an artificial intelligence technique, analyzes massive amounts of data -- internal, external, sensor, social -- to identify and define patterns between complex data. The goal is to discover potentially innovative new solutions. For example, the Singapore government analyzed its video monitoring feeds using advanced analytics such as simulation models, machine learning, and text analytics to uncover, in real time, potentially dangerous situations, such as an unattended bag or a fire in the subway station, in real-time.
While your company pursues data exploration internally, it's also important to look elsewhere to obtain fresh new ideas. By collaborating with an innovation ecosystem -- which can include start-ups, universities, and other companies -- new ideas can be uncovered to innovate and enhance operations. IT professionals would be welcome in such environments.
Universities, in particular, are rich sources of technical and scientific research that have long-term potential in business settings. In fact, our collaboration with MIT was established to conduct research and develop new business analytics solutions to help organizations make informed decisions and solve some of their most challenging problems.
In one of the research projects we worked on with MIT, a new pricing optimization analytics application was developed which helped online fashion retailer Rue La La increase its revenues by 10%. The application, which was based on machine learning techniques and a price optimization algorithm, made data-driven recommendations on prices for flash sale items, resulting in increased sales for Rue La La.
When a company builds an analytics culture and encourages its IT workforce to collaborate with business-side colleagues to explore the art of possibility through data and analytics, it will be in a much better place to fast track innovation and stand out from competitors.