Distinguishing between advanced analytics and business intelligence
Advanced analytics and business intelligence (BI) have more or less the same objective: use data to drive insights that inform business strategy. So what’s the difference?
What is business intelligence?
Business intelligence is an umbrella term for software and services that provide comprehensive yet straightforward insights about an organization’s current state. Think routine reporting or dashboarding, where data is clearly legible for stakeholders to understand month by month. Examples of business intelligence use cases abound, some of which include unifying data to better track marketing leads or to manage shipping operations across a fleet of trucks. Business intelligence is by no means easy, but it is grounded in practical, everyday uses of data.
What is advanced analytics?
Advanced analytics employs the use of sophisticated tools and techniques that surpass traditional business intelligence capabilities. Like business intelligence, it is a wide-reaching term that involves many methods and lends itself to many possible use cases.
Advanced analytics is not meant to replace business intelligence but to augment its efforts. It strives to ask deeper questions of the data, generating insights that not only indicate how the business is currently performing but where its future is headed. If we consider that business intelligence largely aims to point out strengths and weaknesses in current business processes, advanced analytics has the potential to make recommendations and predictions as to how to steer the organization forward.
Examples of 5 advanced analytics techniques
Let’s take a closer look at some of the techniques that fall under the category of advanced analytics. Rarely will organizations need to use all of these techniques at once as a part of their advanced analytics integration; rather, they are merely some of the many tools in the toolkit of a data professional.
Forecasting is the technique of analyzing historical data to predict future outcomes. It considers prior trends to recommend how organizations should plan ahead, such as stocking more inventory for a historically popular sales day. Forecasts can be extremely accurate, but their reliability depends upon the relevance and availability of historical data, as well as the time period to be forecasted.
2. Machine learning
Machine learning is the process of training a computer to predict outcomes without it being specifically programmed to do so. Machine learning models are built to model the desired behavior, and as the model is fed more and more training data, its accuracy in predicting outcomes increases. Data, and lots of it, is the key to effective machine learning models.
3. Data mining and pattern matching
Data mining is the process of uncovering patterns in large batches of raw data for further analysis. Analysts often don’t know what’s in data warehouses or what they should be looking for; data mining techniques, such as pattern matching, help source the right data from data warehouses based upon connections in the data.
4. Semantic analysis
Semantic analysis is the act of determining meaning from text data. By way of semantic analysis, computers can “read” full documents by analyzing its grammatical structure and the relationship of individual words. The technique is particularly useful for marketing teams to be able to analyze social media data or for customer service teams to better understand the effectiveness of online customer support.
5. Complex event processing
Complex event processing is the act of aggregating huge volumes of data to help determine the cause-and-effect relationships for any given event. By matching incoming events against a pattern, complex event processing can shed light as to what is happening.
Benefits of advanced analytics
It’s widely recognized that an advanced analytics integration offers a competitive edge. Just a few of the benefits that advanced analytics can deliver include:
- Better decision-making
Advanced analytics delivers valuable insights that allow organizations to make better decisions, adjust their company strategy, and plan for the future.
- Saved costs
Identifying overspend or leaking costs through advanced analytics can have a huge impact on the budget over time.
- Increased innovation
Through advanced analytics, organizations have developed innovative new products, processes, or sales/marketing strategies that have given them a leg up from the competition.
Challenges of advanced analytics
Many organizations encounter roadblocks along their advanced analytics journey, which prevent them from fully realizing these benefits. According to a 2018 McKinsey survey, “fewer than 20 percent [of companies] have maximized the potential and achieved advanced analytics at scale.” Some of the top challenges of advanced analytics include:
Advanced data analytics will prove its ROI over time, but the upfront costs can be rather costly. Investing in infrastructure and talent, as well as the time required for data strategy and deployment, can be intimidating for organizations to take on.
- Working with data from multiple sources
Effective analytics should employ as many data sources as necessary, but gathering and integrating all of these data sources can be challenging.
- Inaccessible data
Even after the appropriate amount of data is gathered and centralized, if that data isn’t made accessible to the analysts that need to use it, it will serve little value to the organization.
- Skills shortage
Data scientists and data engineers are costly resources and difficult to source. Though user-friendly technologies have lowered the barrier to advanced analytics, many organizations still want a foundational data science team.
- Poor quality data
Harvard Business Review called poor quality data “enemy number one” to machine learning initiatives, and that extends to all facets of advanced analytics. If data hasn’t been vetted to meet data quality standards or properly prepared for the requirements of the analysis at hand, it will only lead to faulty or misleading insights.
Data preparation & advanced analytics
Data preparation accounts for up to 80% of total analytic time. It’s where analysts can encounter a minefield of analytic challenges. But, it also presents the biggest opportunity for improvement. Succeed at data preparation and odds are, you’ll see far less advanced data analytics challenges.
Traditional data preparation methods like extract, transform, and load (ETL) tools or hand-coding are time-consuming and bar analysts from the process of transforming their own data. Recently, organizations have invested in modern data preparation platforms as a part of their advanced analytics integration, which allows organizations to:
- Easily connect to a diverse range of data sources.
- Identify data quality issues through a visual interface.
- Involve non-technical analysts in the process of preparing data.
- Integrate structured and unstructured data of any size.
- Reduce the total time spent preparing data by up to 90%.
Author: Matt Derda