Data Analytics Strategy
Data Analytics Strategy (Data Warehouse, Business Intelligence, Reporting Analytics & Big Data)
Data Analytics predominantly refers to an assortment of applications, from Data Warehouse, Business Intelligence (BI), Analytic Reporting and Online Analytical Processing (OLAP) to various forms of advanced analytics, including Data Mining, which involves sorting through large data sets to identify trends, patterns and relationships; Predictive Analytics, which seeks to predict customer behavior, equipment failures and other future events; Machine Learning, an artificial intelligence technique that uses automated algorithms to churn through large data sets more efficiently; Big Data Analytics, which applies data mining, predictive analytics and machine learning tools to sets of Big Data that often contain unstructured and semi-structured data; and Text Mining, which provides a means of analyzing documents, emails and other text-based content.
Data Analytics Frameworks provide an essential supporting structure for building ideas and delivering the full value of data analytics. These frameworks also provide a set of guiding principles to help you develop your data analytics strategy:
• Practical Concepts: What future outcomes do we want to predict?
Predict future outcomes, understand risk and uncertainty, embrace complexity, identify the unusual, think big
• Functions: Do we have a methodology or process to mature data-analytical requests?
Decide, acquire, analyze, organize, create, and communicate
• Analytics Applications: Which insights are we seeking to generate?
Business insights, sentiment analysis, risk modeling, marketing campaign analysis, cross-selling, data integration, price optimization, performance optimization, recommendation engines, fraud detection, customer experience analytics, customer-churn analytics, stratified sampling, geo/location-based analysis, inventory management, and network analysis
• Skills and Technical Understanding: What skills and competencies are critical for producing new organizational insights?
Data mining, statistics, machine learning, software engineering, Hadoop, MapReduce, HBase, Hive, Pig, Python, C/C+, SQL, computational linear algebra, metrics analysis, and analytics tools (SAS, R, MATLAB)
• Machine Learning: Which business capabilities would benefit from enhanced machine-learned capabilities?
Machine-learning tools, supervised learning, Monte Carlo techniques, text mining, NLP, text analysis, clustering techniques, tagging, and regression analysis
• Programming: What are the most important technical programming skills to mature within the organization?
Python basics, R basics, R setup, vectors, variables, factors, expressions, arrays, lists, and IBM SPSS
• Data Visualization: Which visual representations lead to the best decisions?
Histogram, treemap, scatter plot, list charts, spatial charters, survey plots, decision trees, data exploration in R, and multivariate and bivariate analyses
• Fundamentals: What layer has the greatest potential for transformation—how we make decisions involving presentation, big-data processing, data storage, or the data-connection layer?
Matrices and linear algebra, relationship algebra, DB basics, OLAP, CAP theorem, tabular data, data frames and series, multidimensional data models, ETL, and reporting vs. BI vs. analytics
• Data Techniques: Which data transformation techniques are essential to move us from data to information?
Data fusion, data integration, transformation and enrichment, data discovery, data formats, data sources and acquisition, unbiased estimators, data scrubbing, normalization, and handling missing values
• Big Data: Based on our business architecture, which technology components are foundational to providing intelligent data analytics?
Setup Hadoop (IBM, Cloudera, Hortonworks), data replication principles, name and data nodes, Hadoop components, MapReduce fundamentals, Cassandra, and MongoDB
•Statistics: How do we envision data being categorized and analyzed?
ANOVA, Skewness, continuous distributions (normal, Poisson, Gaussian), random variables, Bayes theorem, probability distributions, percentiles and outliers, histograms, and exploratory data analysis
Different stakeholders will be using your organization’s data for different reasons. Perspectives matter. Data analytics are changing the way company decisions are being made. Data engineering, domain expertise, and statistics each can play a role in the discipline of data analytics for your organization. Understanding concepts such as mathematical techniques is increasingly more important to extract the maximum information from large data sets. Roles you hired for—even two years ago—don’t have the raw skills required to communicate the salient features of data succinctly.
IT Architects provides a service to develop a Data Analytics Strategy based the following key components of a comprehensive data analytics foundation:
• Presentation Layer: Where the dashboards and workflows live
• Data Processing and Analytics Layer: The base for pattern matching, mining, predictive modeling, classification engines, and optimization.
• Data-storage and Management Layer: Data warehouse, operational data stores & relational database systems, scalable NoSQL data storage, and cloud-based storage
• Data-connection Layer: Data sensing, data extraction, and data integration
IT Architects provides a Data Analytics Framework that is segmented into four phases of data analysis:
1. Descriptive Phase – defines what happened
2. Diagnostic Phase – determines why it happened
3. Predictive Phase – forecasts what will happen
4. Prescriptive Phase – identifies what action to take
Together, these phases help organizations classify the types of questions they’re receiving. These can also highlight capability deficiencies within the business and IT. IT Architects makes your data analytics strategy actionable by developing the following deliverables:
• Overarching Strategy: Defines the value and categories of data analytics requirements
• Tactical Plan: Articulates how value will be created using various data analytics components (e.g. Data Warehouse, Operational Data Store, BI & Analytics Reporting, Data Mining, Big Data capabilities, etc.)
• Measurement Plan: Identifies program success metrics, KPIs, and value-add tracking mechanisms for data analytics solutions
• Data Analytics Portfolio Management: Provides an Application Portfolio Management process for data analytics software in order to manage upgrades, contract, licenses, etc. and ensure that the data analytics portfolio of products is implemented and supported effectively.
• Optimization Opportunities Register: Tracks investment opportunities for data analytics and identifies optimization opportunities for existing data analytics capabilities and integrations with the highest probability to achieve the “best” outcome
IT Architects ensures that BI and analytics reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that allow executives, business analysts, and operational workers run their own ad-hoc queries and build reports themselves. IT Architects works with the organization to place control of data analytics in the hands of its users with “self-service” analytic tools where possible.
Using a combination of “big” data and “little” data creates the foundation for quick wins. IT Architects’ philosophy is to start small with little data and build strategically to achieve Big Data analytics success.