Читать книгу Decision Intelligence For Dummies - Pamela Baker - Страница 33

Ah, yes, the bottom-line question on everyone’s lips is cost. Certainly, cost is a major consideration in nearly every business decision. This time, however, it isn’t much of an issue. Because decision intelligence is a rethink and not a redo, you likely already have in place many of the technologies and tools you need. (Think of it as leveraging those items to produce a higher return on investment, or ROI, on what you already have.) Of the tools you may not have, many of the products you need offer free versions or at least free trials so that you can see how they work and whether they’re a fit for your organization.

That may leave a few tools to buy, depending on your current mix of technologies. All told, it’s rarely a huge expense to switch from data mining tactics to decision intelligence.

The following checklist can help you form an idea of some of the technologies commonly used in decision intelligence. That way, you can quickly see what you may need to put on your shopping list — or which functions you might want to hire a third-party who has these things and the experience to use them to do some of this for you.

 Decision modeling software is a part of decision management systems that represents business decisions via a standardized notation — often the Business Process Model and Notation (BPMN) standard — that is used by business analysts and subject matter experts (SMEs), rather than developers. Examples include ACTICO Platform, Red Hat Decision Manager, and FICO Decision Management Platform.

 Business rule management software to manage the business rules in decision-making. Sometimes these are standalone software products and sometimes they are part of a decision management system as well. Examples include VisiRule, Red Hat Decision Manager, SAS Business Rules Manager, InRule, and DecisionRules.

 An AutoML stack or another collection of software capable of automating all or part of the building of ML models. AutoML simplifies the machine learning model developer process by automating many of the more laborious steps, such as feature engineering, hyperparameter optimization, and creating the layers in the neural architecture. Don’t worry if you don’t quite grasp what these automated activities entail because the point in having AutoML is to do all that complicated and time intensive stuff for you. The cool thing is that while AutoML is a useful tool for data scientists, it’s just as useful in democratizing AI. Yes, you too will one day make the AI you want to use in your DI process — by telling AI to make it for you. See, not as hard a concept as you thought. Examples of AutoML vendors include DataRobot, H2O.ai, and Google Cloud AutoML.

 A good data platform which is a technology that bundles several big data applications and tools in a single package. Preferably get one that supports both the creation of algorithms and the delivery of transactional data in real-time. Examples include Google Cloud AI platform, RStudio, TensorFlow, and Microsoft Azure.

 A BI app with natural language processing, AI assistance, and a built-in visualization tool. Examples include Qlik Sense, Domo, Microsoft Power BI, Yellowfin, Sisense Fusion, Zoho Analytics, and Google Analytics.

Members of your data science team will spend most of their time and effort (at least at first) learning how to capture your newly made decision’s requirements using decision model and notation standards such as the Business Process Model and Notation (BPMN), Case Management Model and Notation (CMMN), and/or Decision Model and Notation (DMN) standards.

For decisions where digital data has less of a role or no role, look to the standard tried-and-true array of decisioning tools, like the ones described in this list — and others:

 Mind mapping tools are used to create diagrams to visually organize information, typically from brainstorming sessions or collaboration sessions. Examples of mind mapping tools include Coggle, Mindly, MindMup, MindMeister, Scapple, and Stormboard.

 SWOT tables consist of four quadrants labeled Strengths, Weaknesses, Opportunities, and Threats. Users list line items in each quadrant to clarify considerations (and what might be at stake). SWOT tables can be simple or very complex. There are numerous templates available online if you want to use one.

 Comparison tables are also known as comparison charts. These are typically line charts, bar charts, pie charts, or other types of charts used to compare or contrast data about any number of things such as data fields (expense categories, for example), competitors, or any other item needing a comparative analysis. Examples of these are everywhere online and off and templates and tools to make such charts are available in visualization and BI tools like Microsoft Power BI, Google Charts, Tableau, Chartist. js, FusionCharts, Datawrapper, Infogram, Canva, and ChartBlocks.

 Decision trees depict cascading questions where the answer to one question leads to the formation of the next question. Decision trees are particularly effective in making very complex decisions. They can be simple or very involved depictions, depending on the level of complexity of the problem to be solved. Templates are plentiful online, but there are also tools that will help you make and use them. Examples include Smartdraw’s Decision Tree Maker, Lucidchart’s Decision Tree Maker, and Creately.

 Spreadsheets are those all-too-familiar tools that exist in paper and digital forms, such as Excel and Google Sheets.

 Paper and pencil are the tried-and-true standbys. A simple list of pros versus cons on the back of a cocktail napkin has solved many decision dilemmas and they still work today in some instances.

For smaller organizations and start-ups looking to leverage technology in their decision intelligence processes without investing much money, try starting out with a cloud- or browser-based business intelligence app, or one that’s embedded in software you already have and use, like Microsoft Power BI, which is embedded in Excel in the Microsoft Office suite. You can find many BI apps with free versions as well. If that’s more firepower than you need, check out one or more of the online visualization tools listed above (some are even free!).

One important caveat: Business intelligence (a BI app that produces reports on current and predicted performance of various aspects of the business based on business data analysis) is not the same as a Decision Intelligence process, though BI apps can be used as part of the DI process. A good BI app is simply a quick and reliable way to analyze the data that supports your decision.

The bottom line here is that monetary costs should be relatively small. You may need to spend more on training, however, because your tech people may need additional training on decision theory and the decision sciences — as well as on decision intelligence tactics. Conversely, your business leaders may need that training, too, as well as some training on BI apps to gain a working understanding of data analysis and its full potential.