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!).
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.
Chapter 2
Mining Data versus Minding the Answer
IN THIS CHAPTER
Data driven decisioning is evolving beyond mere data discovery to results-based decision targeting. This newest step on the data science evolutionary chain is known as decision intelligence (DI), a discipline that combines data science, social science, and managerial science into a singularly focused approach to making the best decision possible in any given circumstance.
In this chapter, you find out why targeting an outcome at the outset trumps the traditional model of mining the data first. The result becomes the prime directive in this 180-degree turn in the definition and execution of the oft-sought actionable outcome.
In other words, the focus shifting from discovering information within established data sets to deciding what you most need to know and then actively searching for that knowledge wherever it may reside.
In short, data takes a supporting role rather than a starring role in decision intelligence. The human mind also moves from a role as data/analytics organizer to that of a high-value player in search of the best decision possible. And, last but not least, artificial intelligence (AI) becomes a helpful assistant rather than a dreaded human overlord or job slayer.
In this chapter, you see why this rethinking on how to use data spawned a rising transformative and disruptive force in business as well as in people’s daily lives. You also see why you should be quick to wield this force in any job role you may decide to take on over the course of your career.
Knowledge Is Power — Data Is Just Information
In the beginning, there was data. From the ticking of fingers and toes to stones stored in crude pouches and sticks bundled in vine, the early humans collected and recorded information. This recording of information continued throughout time unabated. The media that was used to record that data changed over the years, according to the technologies of the time. Eventually, however, the data outgrew the number of devices set aside to collect and store it, as well as the number of people using those devices. That’s when we started calling it big data, in a nod toward its bigness overshadowing the capabilities of modern computing.
Folks tend to look in amazement at this growing trove of data, but the truth of the matter is that what seems like an immense resource is merely a mirror we humans are holding up to our world. And therein lies the problem: Possessing information that reflects the world back to us isn’t the same thing as being able to use that information in a practical, real-world sort of way — let alone to do so with any sort of consistent accuracy.
Putting it another way, decision intelligence arrived as a movement when it became evident that mining data is like mining for any other valuable substance: The value lies not in the crude form, but in the polished gem. The goal now is to identify the gem you seek and then go find it. The trick here is do so with the understanding that the work isn’t
