In recent years the amount of data an organization collects, stores and analyzes has grown exponentially.
To address this information explosion problem, organizations are currently spending more on Business Intelligence (BI) tools to try and visualize the enormous amounts of data they currently have.
BI tools are able to collect and curate the data into pie charts, graphics and other visuals quickly both in the cloud and desktop. Being able to visualize your data is a good first step, but it creates the need for organizations to then analyze the data they see and the visuzalizations only provide answers to easy questions like, “How is our revenue performing?” or “What region is contributing the most to our bottom line this quarter?”
However, BI tools only report on the metrics you explicitly tell them to report upon. There is no data analysis performed on data not explicitly specified and hence they will never find insights you were not already looking for in the data. With customer behavior, demographics and influences changing frequently, business need a solution that helps them understand when a customer segment begins to buy products in a new way, indicating an emerging shift in behavior.
Organizations have a plethora of data they collect and store. BI tools only provide visualizations of all that data. To analyze the data would require years of effort and teams of expensive and hard-to-find data scientists.
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What is Automated Business Analysis (ABA)?
Automated Business Analysis leverages AI techniques to analyze data and find unexpected changes in your data quickly, acting as a virtual business analyst. ABA can find these insights immediately reducing the dependency on data scientists and serve up those insights directly to the lines of business owners. ABA platforms automatically surface insights across huge amounts of data, reducing the amount of time required to take advantage of opportunities and fix problems. Automated Business Analysis products work across a myriad of data sources, including cloud-based services and cloud or on-premise storage to perform this analysis quickly.
What type of ecosystem is required to achieve Automated Business Analysis?
Imagine all the data sources that an organization might have and expect to analyze data across. Consider cloud services vendors, like Salesforce or Google Analytics, and database or storage systems like, SQL and Amazon Redshift. The list is ever growing of the ecosystem of partners from which ABA platforms could pull and analyze data across.
Artificial Intelligence as an efficient resource to becoming more data driven
If we continue to create more enterprise analytics dashboards and hire more data scientists, we will continue missing important findings in today’s data where we could have accelerated a marketing program. Important insights that are hiding in the data today such as new customer purchasing trends or new patterns of fraud will remain hidden.
If we do address this problem, it allows organizations to know the impactful moments as they happen so that they can course correct appropriately and identify new segments to target and increase revenue. It allows us to find the golden needle in the haystack quickly and capitalize on it.
Alleviating the analysis choke point with Automated Business Analysis
Many organizations are still of the mindset they can accomplish data analytics with more business intelligence. This is not the case.
The millions of computations alone is more than can be calculated for any one organization. Imagine this simple math problem:
- 10m facets of customer data (web traffic + customer segmentation data by age, gender, or location + product SKU, revenue, etc)
- If 10% of these facets are statistically interesting any given day
- That’s 10,000 insights to consider every day
- How many data scientists does it require to sift through 10k insights a day?
In short, consolidating the data into one place for human analysis can take months or years and there is no immediate value in consolidating the data. The data still has to be analyzed in a timely manner to get value from it. Most companies don’t know where to start on the analysis process and simply use a BI tool to provide a simple-to-use query interface.
The Analytics Value Chain Shifts to Automated Business Analysis
Automated business analysis
Immediacy of insights
Businesses today, especially digitally run businesses, run at lightspeed and need to be able to make quick changes to strategy to leverage an opportunity or divert around disaster. Business have to stay on the pulse of changes in near time and require a solution that will communicate performance-based insights quickly. Additionally, there is a balance for too many insights that can create too much noise and cause analysis paralysis, which means no action is taken because the options of actions are too vast.
Ability to identify occurrence of changes outside the expected
The most important impacts to your business might not be what you are tracking. Some refer to this as Anomaly Detection and in its most simplistic format it could be. This is a requirement of ABA because so many computations need to be calculated quickly and customers do not know where to find these changes. We refer to it as needle in a haystack hunting. Customer realize they have these “needles” hiding in their data, but do not have the bandwidth or vision to always know where or what to look for. ABA only needs to be pointed at data to perform this analysis and create a predictable model about measurements over time.
Cross platform is a requirement of ABA
The truth is businesses don’t operate in data silos. With the explosion of cloud storage, many companies back up databases and large sets of data to the cloud, while they maintain some data on premise as well. Cloud service providers are also prevalent in marketing, finance, and sales data sets. And, the list of cloud service provides increases every year in these business functions. It’s important to see what relationships exist across these datasets and how your business is performing holistically.