AI — artificial intelligence — is all the rage in the world of business analytics. But, focusing on AIGT — all in good time — may serve your organization better, today and tomorrow. Developing and building an intentional analytics progression plan toward AI will not only help your organization now, it will make future jumps into AI and other emerging business analytics capabilities much easier.
Why start with fundamentals? Think about it like diving into the deep end before you learn to swim. You wouldn’t do that! You need to learn to swim before you even go near the deep end of the pool. It’s the same with business analytics. Build a solid data infrastructure first, build your analytics team bench strength, then dive into advanced analytics and AI.
If you’re worried your business is late to the party, don’t be. You’re not! Business analytics buzzwords like “artificial intelligence,” “big data,” and “machine learning” represent exciting opportunities in our field. But they’re still so early-stage that very few organizations are in a position to immediately leverage these cutting-edge fields. There are, however, valuable steps every company can take today to better leverage business analytics, long before these popular buzzwords come into play.
Rest assured, it’s not rocket science — it’s data science! If you’re thoughtful about implementing the fundamentals now, and you’re deliberate enough to do it right, it will pay dividends in the long run. After all, various fields of AI are beginning to transform jobs, revolutionize how companies make decisions, and differentiate eventual winners from losers. You’ll want to be part of that.
Here’s our roadmap to put your organization on the path to better analytical capabilities now and AI readiness in the future:
STEP 1: DATA PREPARATION
Data integrity is essential. Make sure your data is correct. Incorrect data will skew your outputs and business decisions. The old adage “Garbage in, garbage out” is true. Behind the scenes, even the biggest and most advanced companies currently have large teams focused on getting their data organized and accurate. Do you?
- Collect and obtain data through your ongoing practices and from a variety of business functions.
- Save and store your data.
- Organize and define your data. Efficiency and accuracy are essential. Quality metadata — carefully and thoroughly describing what each data element means — is critical to sophisticated business analytics modeling.
STEP 2: INFRASTRUCTURE AND METRICS GENERATION
Once you have data you feel good about, you can stand up the infrastructure to do basic analysis, generate reports, and automate those reports to support decision-making. Many medium and large organizations are at this step today.
- Install core analytical infrastructure. You may choose to stick with tools like MS SQL, SAS, and SPSS, but standing up a slightly more sophisticated infrastructure that includes Python programming software will help speed up your journey.
- Define the right metrics. (check out our recent post How To Fuel Better Decision-Making and Results with Business Analytics)
- Develop reporting. Stand up reporting that’s highly automated.
- Take smart actions using your new reporting.
STEP 3: HYPOTHESIS-DRIVEN DECISION-MAKING
Analytical infrastructure and hypothesis-driven decision-making empowers your business and creates bottom-line impact.
- Bring in the right people with the right analytical skillsets. Tip: You need team players with knowledge about your business, not just experts.
- Develop hypotheses to challenge what you see or test what can’t be analyzed. Proper business analytics will quickly begin bringing value to top-line and bottom-line performance.
- Alter existing business plans and implement new business solutions based on analytical insights.
STEP 4: REAL-TIME DECISION MODELS
Welcome to advanced business analytics!
- Upgrade data and analytical infrastructure as well as skillsets in the team to let computers develop and prove or disprove their own hypotheses to problems you identify. At this point, you will be bringing in statisticians and data scientists who will lead you into advanced analytics.
- Begin leveraging statistical models, robotics, and automation to get smarter, improve segmentation, optimize decisions, and free up your valuable resources to do more with less.
STEP 5: AI
You’ve arrived, and you did it AIGT!
- The fact of the matter is that AI remains in its infancy. We know that the promise of AI enables computers to determine the problems and the solutions themselves.
- Once you arrive, there will be a wealth of case studies and experiences from others, and your new team members will help you make the most of this exciting new field!
Ready to follow the roadmap to put your organization on the path to better analytical capabilities now and in the future? To get started, figure out where you stand today, and set a realistic goal for where you want to be two years from now. Then implement your plan by collaborating with business analytics experts — like Spinnaker, to help you get there faster.
Want to learn more? Keep an eye on Spinnaker’s blog as we explore business analytics and its importance in delivering real bottom-line value across your business. Follow Spinnaker on LinkedIn and receive updates in your LinkedIn feed.