The understanding that AI and predictive models bring hidden benefits is important to an enterprise. Years of collected data points that go unused are valuable benefits wasted.
Organizations like The Law Society of British Columbia have implemented data analytics systems to reap its benefits. Some of the benefits include autonomous data risk assessment by leveraging technologies like Dataiku and a team of knowledgeable data scientists.
One of the most common use cases of data analytics for an enterprise is risk assessment. Through the use of correct technologies and savvy analysis, it is possible to create autonomous systems for business-wide impact. One example includes the risk factor assignment of clients based on past data.
Anomaly detection, specifically, allows the finding of outlying consumer behavior. This results in the recognition of changing consumer patterns, potential opportunities, or obvious signs that were hidden in the data. Additionally, anomaly detection can be extrapolated into other processes that result in immediate business benefits. AI and analytics also provide many ways to automate currently manual analysis processes.
Anomaly detection requires previous data
Anomaly detection typically occurs after some cause that prompts alert. Through the use of AI, anomaly detection can be instead, a preventive cause. AI interfaces can be customized to alert when new patterns of behavior occur in an organization. The only requirement: previous data.
To begin taking advantage of these systems and leveraging technology to identify patterns and behaviors, only previously collected data is necessary. Once data has been localized for use you’re ready to define the business problem needing to be solved, improved, or analyzed for new benefitting patterns.
The second step involves creating a team. Usually, the initial part involves a group of stakeholders capable of defining the business problems to be solved.
The third step involves creating a more specific team composed of data scientists and data analysts. This team is responsible for choosing the correct software solution to process data into meaningful business processes and to have the troves of data in a legible format. Usually displayed through an interface in the form of reports and charts.
The fourth step involves the creation of business objectives by using the S.M.A.R.T. methodology of goal setting. This provides an efficient way to measure the benefits of this whole process before more time is invested.
Step five involves the assembly of all the project stakeholders. This includes the technical experts such as data scientists, analytics, project managers, and the business-related decision-makers previously assembled.
The five-step process ensures the initial bedrock of processes and processors are able to transform data into practical enterprise-impacting results.
Use cases and enterprise benefits
Enterprises may benefit differently from data analytics, future financial conditions, and the risks involved.
Predictive analysis can provide a myriad of previously hidden trends and factors. Using the example of The Law Society of British Columbia, they have opted for risk analysis given previously recorded law firm data. The negative behavior patterns from the law firms have provided the society a risk factor of low, neutral or high. This can be extrapolated into anomaly detection, lead heat level, and many other uses.
Benefits of Dataiku for predictive analytics
We believe one of Dataiku’s most valuable features is its flexible tooling, which can create solutions to multiple applications across multiple platforms. They’re also a well-established data science company used as data storage in data scientists’ environments like Big Data and NQA. Through this tool you have the opportunity to learn more about their solutions, their approach to their workflows, their use cases, and the amazing tools they come up with as they work to improve their product, as well as their insights. There are several benefits of building predictive analytics solutions with Dataiku:
- Cloud access ensures company-wide access into data as well as control.
- Enhanced ETL (data extraction, transformation and loading) processes, allowing helpers for data cleaning, extraction, and transformation.
- A selection of AI models form a common interface and are easily interchangeable for comparison.
- Model deployment with Kubernetes into the cloud under a common application.
- Model upkeep and automatic monitoring metrics of performance for optimal business impact.
Dataiku enables data-driven decisions, helping enterprises be more agile and efficient with the massive amounts of data they hold from customers or for internal purposes. Dataiku allows the efficient operation of data teams by concentrating efforts and automating models from pre-built solutions.
AI Maturity and Readiness
We’ve seen this before. 20 years ago, traditional enterprises saw a significant shift with the acceleration of software development, with industry leaders like Amazon redefining what it looked like to embrace software solutions. Forbes reported that companies that embraced software in 2011 remained the “current market leaders in their respective fields” and that “the top five market capitalization companies worldwide in the second quarter of 2019 were offering some type of software solutions.” During that time, companies faced the same questions that leaders consider with AI transformation today. What is the state of our efforts in this space? How far does my business need to go to be successful and competitive in my market? What are the next steps to get there?
Leaders are asking the right questions and preparing themselves to be open to a variety of results. Gathering information to gain diverse perspectives and identifying potential options is the best first step. Just as enterprises used the Capability Maturity Model to assess software maturity decades ago, today we find an AI Maturity Model or a readiness assessment to be the best way to quickly get a snapshot of where you are and where you’re headed.
Don’t Guess, Assess
An AI Maturity framework is essential for leaders interested in seeing how their own components of AI maturity measure up. There are several different approaches to the AI Maturity model, including the definition of various components and capabilities that make up the AI competency within an organization. Leaders should look for a model that has understandable components and fits their existing models so they can easily identify areas of improvement or apply recommendations that surface. There are several assessment options that we’ve found to be an effective first step.
Dataiku’s AI Maturity Survey
Dataiku has a plethora of tools and resources available in its ever-evolving Learning Center. One tool that Dataiku created specifically related to AI maturity is the AI Maturity Survey. This tool is a quick guide that helps you assess your company’s AI maturity, compare your progress against other companies, and get recommendations tailored specifically to you. This survey focuses on the components of vision, data, talent, value, governance, and systems and produces a chart rating your results against average scores.
The AI Maturity Board Game
AI maturity isn’t a game, but it does include a level of risk and strategy that need to be balanced in order to see optimal results. Snow Fox Data partnered with Dataiku for an event where clients had the opportunity to play an AI Maturity board game. This game is an engaging and interactive way to experience how various components can impact the path to AI Maturity. Let us know if you’d like to give it a try!
Data Analytics Strategy Assessment
For leaders looking for a deeper understanding of how AI can impact their unique organizations, we recommend a Data Analytics Strategy Assessment. This assessment takes a detailed look at a variety of components, specifically the people, processes, tools, and roadmaps necessary to execute data science and analytics initiatives. It is a self-evaluation that identifies where the most impact could take place and provides the strategic next step for your unique business objectives. Setting this tailored baseline for your organization can ensure your path is sustainable and practical as you grow.
Assessments provide awareness and help minimize the guesswork as you choose a path forward. Once you identify where you fit into this framework, you can better understand what activities you should perform to influence change and create momentum. For example, one company’s assessment may reveal advanced use cases and nearly perfect data. It may also show a gap in the ability to perform data science or build AI solutions at scale, which would indicate an investment in technology might be necessary. Another company may already have the latest tools and data resources but could be missing the people component necessary to execute their initiatives. Leaders use tools like these to make informed choices on where to invest to maximize maturity and get the best business value.
Set Your Business and AI Maturity Priorities
While your assessment serves as a baseline, the next step in your AI journey is unique only to you. The outcomes and priorities for moving forward will be specific to what works best for your organization. Leaders must get the entire team on board with the priorities of the business so that the AI initiatives can support them. Maturity takes time and engagement across the organization. This is especially true with key stakeholders who are critical to driving these AI initiatives forward long term.
The priorities you choose will set the stage for your entire AI journey and greatly impact how quickly and effectively you can make progress toward your goals. For example, a company that chooses to prioritize rapid change over a deep understanding of its own AI maturity can find itself running into several challenges. They may start building AI solutions and find that these solutions can’t be scaled (maybe a people component of the process was overlooked) or governed (with a process component missing as part of the strategy). Businesses can waste time and money when focused only on one area and miss key opportunities to scale at various levels to balance and support other components of their AI efforts. Halting an initiative to go back and fix the missing components can waste valuable time and resources for your organization.
Organizations that choose to implement initiatives that are as fluid and new as AI may look back to find that without additional guidance along the path to AI maturity, they have not matured at all. Evaluate the effectiveness of your priorities with these questions:
- Are you continuously working on just one project? If you have been working on the same use case for months or even years and it still isn’t complete, this is a red flag that you’re not moving forward in your plan (or your AI maturity).
- Are your solutions used and trusted? If you have created an AI solution that isn’t being used or isn’t seen as a trusted resource, then your initiatives are not maturing and the value is not increasing at scale.
- Are you being asked for more? Organizations that are doing it well are alarmed by the immediate stardom these efforts have gained across the business. People will be curious and clamoring to execute the next effort because of the valuable results you’ve already proven.
Enterprises find the most value in working with an advisor to take intentional steps toward AI maturity. Companies leveraging industry expertise are building and implementing strategic AI solutions in their organizations and making informed and even automated business decisions. This not only gives them a competitive edge in the marketplace but also makes them a healthier company overall.
AI Is Here. The Next Move Is Yours.
The AI and ML world keeps moving and we are all on this wild ride together. While organizations learn and grow through these advancements, each journey through the AI Maturity model will be unique. In the driver’s seat, you get to decide what AI maturity looks like for your business and can move confidently to your next step with industry experts, tools, and resources here to guide you along the way.
About the author
With over 20 years of experience as a Lead Software Architect, Principal Data Scientist, and technical author, Ryan Moore is the SVP of AI and Analytics at Amplifi, and our resident Dataiku Neuron. He provides Data Science architecture and implementation consultation to organisations around the globe.
