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Продвинутая аналитика как конкурентное преимущество10 октября 2013 Все больше организаций осознают выгоды от применения инструментов продвинутой аналитики («advanced analytics»). Чтобы инвестиции в решения бизнес-аналитики были рентабельными, стратегически важные решения компании должны основываться на информации, полученной в результате анализа данных. Таким образом, аналитика может помочь принимать правильные решения и ускорить развитие бизнеса. Подробнее о типичных проблемах, возникающих в процессе использования информации, а также о выгодах от их устранения с помощью решений продвинутой аналитики узнайте в данной статье. (Материал опубликован на английском языке)
By and large, the primary challenges that organizations face today in the context of advanced analytics are:
- “Big Data” deluge
- Information silos
- Skills shortage
The organization that can overcome these challenges, and have the ability to address them consistently with speed and accuracy, has the edge over its competition. Such an organization is considered a matured analytical organization – the Analytical Competitor.
In order to combat the Big Data deluge, organizations need a lean analytics life cycle that reduces data-to-decision cycle and more importantly reduce data-to-decision latency. Everyone gets it – they need to analyze the data and build predictive models so that they can influence business outcomes.
But the influence is limited if it’s operated in silo and batch mode. The biggest impact and influence comes when you operationalize the predictive model. What does that mean? It’s all about embedding predictive models into the application, business processes, and line of business solutions. That is, you’re moving the predictive model closer to the end consumer and decision makers.
There’s a huge skills shortage for knowledgeable analytical professionals (data scientists). It’s expected to get worse, not better. On one hand you have human experts who has deep understanding of business; And on the other hand you have analytical experts who deep understanding on analytical techniques. And there’s a huge gap between human experts and analytical experts. There is tremendous need for tools and platform that can bridge the gap between human and analytical experts.
To combat all these challenges, organizations need a broad spectrum of tools, platform, and solutions for analytical needs. They also need a platform that supports wide range of analytical application development, effective communication across departments, and strong executive leadership support for advanced analytics.
Meeting the Challenges with SAP Solutions
Having said that, the SAP advanced analytics portfolio solves those macro issues with its focus on four fundamental key pillars :
- Reduce decision latency with advanced analytics
- Operationalize predictive and optimization models across the enterprise
- Bring predictive analytics to a broad spectrum of users
- Provide integrated, open, and flexible platform
- With the four pillars addressing those challenges, SAP is able to take advanced analytics to the next level, and more importantly, bring advanced analytics closer to the business user.
The typical analytical life cycle involves:
- Defining business problem / opportunity
- Data preparation
- Data discovery/exploration
- Model development
- Testing and validation
- Model deployment
The majority of time is spent on the data preparation. Data-to-decision latency is increased by the complexity in the analytical life cycle, data preparation, inadequate tools for each person in the cycle, and organization alignment.
The analytical life cycle is iterative and interactive in nature. Various people from different backgrounds and skills are involved at various stages of the process. Hence, the resulting predictive models are organization assets, and reducing the time from data to decision requires a combination of technology, people, and process.