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The A2Go Blog

How to Use Analytic Data Models to Plan and Prioritize Your Analytic Roadmap  

by Jack Gau, on Feb 15, 2018 1:26:50 PM

This blog post is the first of my series, Analytics at a Crossroads. Analytics2Go believes that all organizations—whether big or small—need data analytics to improve their business decisions. Organizations applying analytic techniques outperform organizations using ad-hoc decision-making methods by at least 10% initially. So, where do you start, and how can you prepare your business for your next big data analytics project?

First of all, there is no need to delve into technical questions before answering the what and why questions of using analytics in your business. Some basic principles include a methodology that focuses on business priorities, a coherent approach that leads to faster results, and the involvement of all stakeholders and experts—within and outside the company. This phrase rings true: Making business decisions without analytics will soon seem as unusual as shopping without Amazon.  So, what are the business layer issues you may want to consider?  

 

4 Key Questions to Answer Before Diving into Big Analytics

  1. Which key performance areas should I focus on?
  2. What needs to be optimized for each KPI?
  3. How will my business process change?
  4. How will new processes using analytics be adopted?

With these questions in mind, you are now ready to look at how analytics can help meet these business objectives by translating your business strategy into a big data analytics strategy.

 

Transform Your Business Strategy into a Big Data Analytics Strategy

  • Which key horizontal capabilities do I need to build?
  • How do I build them over time?
  • What are my organizational goals?
  • How much will need to be invested in time and money?
  • Last but not least, what is the business case?

At this point, most businesses learn how to develop and deploy reliable best-practice techniques for applying advanced analytics from leading companies. Start by considering vastly more decision alternatives, scoring these alternatives based on accurately predicted outcomes and recommending optimal decisions. 

Analytics are being applied by enterprise companies at a fast growing rate. These companies have mastered many of the data science techniques; they have been able to develop the data science teams and technologies required to support big data analytics. They recognize that analytic-enabled decision making routinely outperforms so-called “ad-hoc” decision making by 10% or more. 

Enterprise companies use analytics to predict the outcome of their decision alternatives before they make decisions, which is a huge business advantage. Many of these companies also expand the number of decision alternatives that they consider for complex decision making by several orders of magnitude—from a handful of intuitively-identified alternatives to millions of systematically-defined alternatives. Smaller companies can not usually afford to invest in the skilled data science specialists and all of the supporting human and technical infrastructure, which isrequired to carry out data science analysis commercially.

 

Analytics2Go’s Augmented-Analytic Solution 

Enables companies to:

  • Apply analytic capabilities without hiring expensive data scientists
  • Avoid big-data sourcing challenges
  • Outsource responsibilities for data model maintenance and performance
  • Deliver optimal decision recommendations when and where they are needed.

Creating and using data models is vital during this analytics strategy process. These data models can be used by companies to formulate potential analytics initiatives, to identify potential drivers of the business, to set analytic priorities, and to achieve optimization objectives for the business. Armed with insights gleaned from the analytics strategy process and the set of data models that are generated, companies can then move on to the technology considerations that enable them to capitalize on new analytical capabilities.

 

4 Key Technology Areas of Expertise

How should I acquire and manage the data? How should I enable data science and analytics experts? How can analytics be democratized with end-users? How can I reduce the time it takes to evaluate and integrate with business applications?

There are four areas of expertise companies either need to assemble in house or acquire from outside of the company to effectively use analytics:

1. Data Management

  • Data collection and cleansing
  • Data integration, mashing
  • Information management
  • Scaling and security
  • Physical storage and cloud options

2. Visualization

  • Executive dashboards for KPIs
  • Granular drill down
  • Real time transactional
  • Sharing and collaboration

3. Data Science

  • From simplest to most sophisticated
  • In-house versus outsource
  • Scale, variety and complexity
  • Knowledge capture

4. Integration

  • From concept to production
  • Enabling business processes and downstream business applications
  • Soliciting feedback
  • Operating models and governance

The final outcome of the above efforts is the creation of a comprehensive plan: your analytic roadmap, which contains analytical components that are based on a multidimensional sequential project plan. Each phase details new implementations of the platform and technologies; data and governance; skills and capabilities, and business outcomes. Do not get stuck in the past; bring your business into the future with data analytics.

Topics:InsiderAugmented AnalyticsData Analytics

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