2. Data technologies
Data entry level tools
CRM
Track customer behavior throughout the sales cycle to understand current needs and predict future needs.
Website and social analytics
Analyse website and social media traffic and user behaviour to evaluate effectiveness of your digital strategy and gain market insights.
Web based VOIP
Monitor source and destination of a call as well as call agent performance to evaluate a comprehensive range of products and services.
Making the most of your CRM
MAXIMISE DATA CAPTURE
Make the CRM software easy to use. Find out what each department really wants the CRM to do, and customize it.
ENSURE QUALITY OF DATA
Integrate as many data sources as possible. Identify other client-facing systems that can be analysed, especially social media.
Keep it up to date. Have a consistent process for updating
ANAYSE THE DATA
Define the questions that you want answered. Then work backwards to determine the right analyses.
Website and social analytics
Gives us rich and insightful data
VOIP analytaics
Use call tracking analytics to:
- Manage call flow
- Gain customer insights
- Identify opportunities for correction and/or need for skills training
- Recognize high-performers
Use speech analytics to gain qualitative insights into customer satisfaction.
Big Data Tools
Scalable network of servers
Allows developing a small scale operation, that can then be expanded upon.
Eg. Cloud based services
Framework for data management
Enables a structure to easily manage storage and handle different data flows and formats.
Eg Hadoop based software.
Analytics software
Software with strong data vizualization capacities can help convey meaningful insights of data faster.
Eg Tableau, SAS, SPSS, etc.
Spotlight On HADOOP
What is it?
A software technology designed for storing and processing large volumes of data distributed across a cluster of commodity servers and commodity storage
What does it include?
- Hadoop Common: The common utilities that support the other Hadoop modules,
- Hadoop Distributed File System (HDFS): A distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster,
- Hadoop YARN: A resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users’ applications,
- Hadoop MapReduce: A programming model for large scale data processing
How do companies use it?
Hadoop lends itself to almost for any sort of computation that is very iterative, scanning TBs or PBs of data in a single operation, benefits from parallel, and is batch-oriented or interactive. Processing Organisations typically use Hadoop to generate complex analytics models or high volume data storage applications such as:
- Risk modeling,
- Retrospective and predictive analytics,
- Machine learning and pattern matching,
- Customer segmentation and churn analysis
- hadoop.apache.org
Spotlight on spark
What is it?
A general-purpose distributed data processing engine.
It’s similar to Map Reduce and other data processing layers built on top of HDFS in Hadoop.
What advantages does it have over Hadoop?
Hadoop is based on batch processing where the processing happens of blocks of data that have already been stored over a period of time. The USP for Spark was that it could process data in real time and was about 100 times faster than Hadoop MapReduce in batch processing large data sets.
How do companies use it?
Stream processing: processing and acting upon the data as it arrives. Streams of data related to financial transactions, for example, can be processed in real time to identify– and refuse– potentially fraudulent transactions.
Machine learning: its ability to store data in memory and rapidly run repeated queries makes Spark a good choice for training machine learning algorithms to identify and act upon triggers within well- understood data sets before applying the same solutions to new and unknown data.
Interactive analytics: Rather than running pre-defined queries to create static dashboards of sales or production line productivity, analysts explore their data by asking a question, viewing the result, and then altering the initial question slightly or drilling deeper into results.
Spotlight on NoSQL
What is it?
Traditionally, the software industries use relational databases to store and manage data persistently. However, large volumes of data sets have introduced new challenges for data storage, management, analysis, & archival. In addition, data is becoming increasingly semi-structured. To resolve these problems, a class of new database products has emerged consisting of column-based data stores, key/value pair databases, and document databases. “Not only” SQL or NOSQL refers to all databases and data stores that are not based on the Relational
Database Management Systems (RDBMS) principles. It does not represent single product a group of products and a various related data concepts for storage and management.
What advantages does it have over RDBMS?
Data is stored in a host of different databases, not requiring only rows and columns, with different data storage models.
RDBMS follows fixed schema: the columns are defined and locked before data entry. NoSQL Follows dynamic schemas; you can add columns anytime. Scaling an RDBMS across multiple servers is a challenging and time-consuming process. NoSQL supports horizontal scaling across multiple servers.
Spotlight on R
What is it?
R is a statistical programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
What does it include?
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
- data handling and storage facility,
- large, coherent, integrated collection of intermediate tools for data analysis,
- graphical facilities for data analysis and display either on-screen or on hardcopy, and
- well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output
How do companies use it?
R is well-suited for scientists, engineers and business professionals because of packages covering a wide range of topics such as econometrics, finance, and time series as well as tools for visualization, reporting, and interactivity. In particular R can produce business-ready reports and machine learning-powered web applications