Analytics has become an integral part of businesses and organizations. With data becoming more accessible, analytics is being used to gather insights and drive better decision making. There are two main types of analytics descriptive analytics and predictive analytics. Both have their own unique applications and benefits.
What is Descriptive Analytics
Descriptive analytics, as the name suggests, describes what has happened in the past. It looks at historical data to understand past performances and patterns.
Some key characteristics of descriptive analytics:
- Focused on past events and data points
- Helps answer “what happened”
- Used to evaluate past performances
- Analyzes structured historical data
Common descriptive analytics methods:
- Reporting
- Visualizations
- Dashboards
- Summarizations
Key metrics in descriptive analytics:
- Averages
- Frequencies
- Distribution
- Measures of variation
Benefits of Descriptive Analytics
Some benefits of using descriptive analytics include:
- Understand performances: Analyze sales, marketing campaigns, operations etc. to know what worked and what didn’t. Help assess productivity and efficiency.
- Identify areas for improvement: By knowing weaknesses in the past, companies can improve in those areas.
- Track progress: Analyze progress towards goals and KPIs through historical comparisons.
Overall, descriptive analytics empowers organizations to make data driven decisions aligned to past performances. It takes a rear view approach with insights from the past powering the future.
What is Predictive Analytics
As opposed to descriptive analytics, predictive analytics focuses on the future. It analyzes historical and current data to predict unknown events and behaviors.
Key aspects of predictive analytics:
- Leverages machine learning and data mining
- Makes statistically driven predictions
- Forward looking to determine “what could happen”
- Requires extensive, structured and unstructured data
Some common predictive analytics applications are:
- Forecasting sales
- Predicting customer churn
- Projecting demand
Predictive analytics employs techniques like:
- Regression analysis
- Machine learning
- Data mining
- Statistical modeling
- Forecasting
Benefits of Predictive Analytics
Here are some key advantages of using predictive analytics:
- Prepare for future: By understanding probable future scenarios, companies can proactively prepare instead of reacting.
- Early warning signs: Identify risks, threats and opportunities early for timely action. Predict customer behaviors to reduce churn.
- Competitive edge: Companies can anticipate market changes in advance to adapt quickly. They can understand emerging consumer needs faster.
- Improve decisions: Analytics decisions on product development, marketing strategies, inventory etc. tend to be more prudent as they are backed by data.
With predictive analytics’ future oriented approach, businesses can drive innovation and growth based on data backed decisions. It empowers organization to peek into the future by evaluating probabilities.
| Descriptive Analytics | Predictive Analytics |
|---|---|
| Backward looking | Forward looking |
| Focus on past events and reasons | Focus on predicting future probabilities and trends |
| Answers “what happened?” | Answers “what could happen?” |
| Mostly simple math and statistics | Advanced statistical modeling and machine learning |
| Well defined problems | Exploratory approach to open ended problems |
Table 1: Key differences between descriptive and predictive analytics
As seen above, while descriptive analytics looks back, predictive analytics looks ahead. Descriptive analytics has well defined goals while predictive analytics follows an exploratory approach.
Descriptive vs Predictive Analytics: A Use Case
Let’s take an example of a retailer looking to improve sales performances across store locations.
The retailer can adopt descriptive analytics to:
- Analyze past sales data for different locations
- Identify best and worst performing stores
- Examine foot traffic across stores
- Track previous promotional campaigns’ impact on sales
These insights will reveal reasons behind variances in historical store performances.
On the other hand, predictive analytics can help the retailer to:
- Predict sales for next month across stores
- Forecast store traffic for upcoming holiday season
- Identify locations at risk of missing future sales targets
- Model the possible impact of a new seasonal menu on sales
Such analytical insights help the retailer prepare for variances in advance. The retailer can tweak marketing initiatives and inventory planning based on these predictions to maximize future sales potential.
Thus, descriptive and predictive analytics fuel data backed decisions from different perspectives. While one provides reasons behind historical performances, the other enables data-driven planning for the future. Retailers need both these lenses to drive consistent growth.
Choosing Between Descriptive and Predictive Analytics
The choice between adopting descriptive or predictive analytics depends on organizational objectives and data strategy maturity.
Organizational Goals
Companies focused on improving past performances by learning from historical experiences find descriptive analytics more useful. Those looking to prepare for future uncertainties based on predictive intelligence prefer predictive analytics.
However, most successful companies leverage both techniques. Descriptive analytics guides short term decisions while predictive analytics informs long term planning and innovations.
Data Strategy Maturity
Beginners with limited data availability or quality rely more on descriptive analytics for straightforward reporting. On the other hand, predictive analytics requires extensive high quality historical data. It also needs significant data wrangling capabilities.
As data capture and processing improves, companies can graduate to advanced predictive analytics. This provides richer insights as predictions get more precise with increases in data volume, variety and velocity.
In most organizations, descriptive analytics lays the foundation before predictive analytics can be meaningfully adopted. The success of predictive analytics depends directly on foundations established by its descriptive counterpart.
Therefore, prudent organizations first invest in developing robust data collection and descriptive analytics capabilities. Only once past performances are tracked reliably do they branch out towards exploratory predictive analytics.
Descriptive and Predictive Analytics Together
Instead of choosing one over the other, most mature analytics teams leverage descriptive and predictive analytics in concert.
Descriptive analytics streamlines information from the past while predictive analytics interprets patterns for the future. Key processes where these two methodologies complement each other include:
Establishing KPIs
By analyzing past data, organizations identify core metrics that represent their business success using descriptive analytics. These key performance indicators (KPIs) serve as foundation for predictive models to drive desired outcomes like higher revenue or net promoter score (NPS).
Forecasting and Planning
Predictive analytics enables realistic forecasts that consider both historical patterns and emerging trends. It accounts for past seasonal impacts uncovered by descriptive analytics while projecting beyond them. Such comprehensive insights allow organizations to develop sound strategic and operational plans.
Impact Analysis
Once strategies get implemented, companies can review their impact both historically and predictively. Descriptive analytics tracks tactical outcomes to gauge effectiveness. Predictive analytics then models the potential future impact of continuing such programs through extrapolation.
Risk Management
Analyzing past incidents and complaints shows current vulnerabilities. Predictive analytics allows establishing probability of occurrence for different risk events. It also estimates expected monetary and non monetary impact spanning dimensions like customer loyalty. Such probabilistic information guides optimal resource allocation for risk mitigation. Thus, companies get the best of both worlds by embracing descriptive and predictive analytics collectively based on their competencies and needs.
The Future of Analytics
Going forward, adoption of analytics is expected to grow exponentially as its potential gets unleashed further with latest technologies.
As per Gartner, by 2025, over 50% of global mid size to large enterprises will be leveraging both descriptive and predictive analytics concurrently. In the retail sector particularly, analytics penetration is poised to reach almost 100%.
Advancements in machine learning, IoT sensors and cloud platforms are disrupting present analytics approaches by handling data better. As organizations mature in extracting value from data, prescriptive analytics is also rising. It recommends data driven decisions optimal for business success using ML algorithms.
Here is the continuation of the article:
Adopting Analytics: Key Aspects
As predictive analytics becomes mainstream, most companies aim to exploit it fully along with descriptive analytics. Here are some essential aspects to consider for successful analytics adoption:
Integrated Infrastructure
The technology foundation needs to support seamless descriptive to predictive model progression. Cloud based data platforms provide such agile, unified architecture for custom analytics application development. They also facilitate quick deployment across enterprises.
Centralized Data
Reliable analytics require integrated, trustworthy data from diverse sources including IoT sensors, apps, social media feeds etc. Using pipeline and datastore tools, raw data must get ingested and refined appropriately before analysis. Master data also needs governance for consistency.
Business Alignment
Analytics objectives should directly serve business goals through relevant KPIs. Outcomes focused on customer lifetime value or lead conversion rates provide much higher RoI compared to vague reports. Such strategic alignment requires coordination between executive sponsors, analysts and line of business teams.
Continuous Learning Culture
With exponential data growth, insights can rapidly become outdated. Organizations must continually experiment, audit outcomes and enhance analytics models through modern ML techniques. More user friendly analytics tools are enabling such agile, democratized learning.
Therefore, integrating descriptive and predictive analytics strategically as core business priorities is crucial. For long term returns, these emerging capabilities need adequate executive participation along with infrastructure and skill upgrades. Prioritizing use cases with highest impact value accelerates measurable analytics driven transformation.
Conclusion
Descriptive and predictive analytics offer complementary lenses for evidence business planning suited for organizations at different data maturity levels. Descriptive analytics enables learning from historical performances through metrics like sales fluctuations. Predictive analytics then uncovers forward looking trends and behaviors to anticipate outcomes.
Leading companies implement both techniques concurrently to inform strategies for short and long horizons through actionable analytics. Integrating these models seamlessly alongside business goals necessitates robust data pipelines, governance and modeling expertise.
With the analytics landscape rapidly advancing in accuracy and usability, adoption is set to accelerate. As predictive analytics becomes mainstream, even prescriptive variants will emerge for optimized decision recommendations. At the core though, descriptive analytics will continue providing the contextual grounding to unearth patterns for tactical forecasts and simulations. Rather than competing, these two approaches together unlock analytics ROI fully.
FAQs
What are the limits of descriptive vs predictive analytics?
Descriptive analytics is limited to explaining historical performances. Predictive analytics gives probabilistic guesses susceptible to more inaccuracy due to statistical assumptions. Both can produce unreliable insights without adequate data.
What skills do descriptive and predictive analytics require?
Descriptive analytics employs basic statistics for aggregation and visualization to business teams. Predictive analytics demands advanced machine learning and programming expertise for modeling.
Should companies adopt descriptive or predictive analytics first?
Most organizations begin with descriptive analytics to study past trends required for identifying problems worth predicting solutions for using predictive analytics.
Can descriptive and predictive analytics integrate?
Yes, they provide complementary insights descriptive reveals why past events occurred while predictive anticipates scenarios for the future. Integrating them builds higher context for strategists.
Which analytics method aligns better with company objectives?
Descriptive analytics suits tactical goals involving short term execution tracking. Predictive analytics serves long term strategies focused on preparing for market changes driven by emerging behaviors.
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