Integral Net Information Systems Limited

Big Data has become a reality that cannot be ignored. In one of my previous posts, I explained the reason for the adjective big that sits before data to create big data. I mentioned that big is not just refereeing to volume, but also to variety and velocity of growth.  Big data not only is big in size, but is fast in getting bigger and it covers a variety of data sources that exceed the boundary of the existing relational systems such as CRM  and ERP.

I thought may be a post to give some examples of unstructured  big data may interest many readers, so below I have bullet pointed some and  I may update this post  in future when I come across more examples.

• Detailed machine generated data, such as equipment logs, RFID tags
• Sensor generated data such as in manufacturing, metering, condition monitors
• Web related data, such as visitors, hits, keywords, times, etc
• Social Media data such as twitter, facebook comments, feedbacks, etc
• books, journals and text base documents
• Scanned records
• Audio files
• Video files
• GIS and map related data files
• Body of  email messages
• Web page contents such as static pages or blogs and wikis
• Image data such as spatial and auto cad images

Agile focuses on delivering value. Business Intelligence is also about providing means to deliver value. But how value is defined?  Value may be different for internal  organisational units within an organisation, different roles within the same organisational unit,  or even different times. So to deliver value via business intelligence is not a static process. It involves change and this is where Agile BI comes into the picture.

To my opinion first of all Business Intelligence should not be considered as a project. I explain: Business Intelligence is about making sense of organisational data via accessing a well trusted delivery model that suits best for the domain and type of the user with  the supporting underlying infrastructure. Now it is simple: Data changes, people change, processes change, businesses merge or separate, systems merge or separate, teams combine, groups divide, processes streamline to accommodate all these changes, the new data enters the cycle, and some data seizes to be important of of having any value as the result.

This is why Business Intelligence and Management Information is no longer a programme, it is a product group and it is in fact an evolving product group with subgroups for handling many layers that Management Information covers. This is a portfolio of products that requires project or programme management for continuous improvements!

Agile can handle this complexity, because agile concentrates on delivery of  higher value on  repeated short time frames and keep reviewing the product items by adding what is recognised as value for the whole organisation.  In short Agile BI  is  Management of MI Requirements by Reality!

Big Data is changing the way we need to look at Enterprise Data Warehousing. Previously I posted about big data  in Big Data – Volume, Variety and Velocity!. I also posted about the supporting projects from Apache Hadoop, such as Hbase and Hive in Big Data, Hadoop and Business Intelligence. Today I want to introduce a new concept, or better say an original idea. Big EDW!  Yes, Business Intelligence and Data Warehousing also will have to turn to Big BI and Big EDW!

So what makes the fabric of Big EDW and Big BI Analytics? The answer is the ability to analyse and make sense of Big Data, which covers not only the 20% of the structured data that organisations keep on their relational and dimensional databases, but also the vast remaining 80% unstructured data scattered in digital and web documents such as Microsoft Word, MS Excel, MS PowerPoint, MS Visio,  MS Project, as well as web data such as social media, wikis, web sites and other formats such as pictures, videos, and log files. I have posted about the meaning of unstructured data  previously  in On Unstructured Data.

Traditionally Enterprise Data Warehouse is a centralised Business Intelligence System, containing the required ETL programs to access various data sources,   transformation and load into a well designed dimensional model.  The front end BI access tools such as reporting, analytical and dashboards then is used on their own or integrated with the organisations interanet, to give the right users timely access to relevant information for analysis and decision making activities.

The Big Data does not quite  fit into this model for three main reasons, volume, variety and velocity of change and growth. Big EDW will need to break some of the traditional data warehousing concepts, but once done, it will create value that has many folds of magnitude.

Big EDW, should have the ability to be quick and agile in dealing with Big Data. It has to make it available for quick access to many new available data sources  in high volume. Enhanced design patterns or new use cases  have to emerge to make this possible. These patterns and use cases  should make use of more intelligent and faster methods of providing the relevant data when  required. This could be achieved by many methods such as  dimensional modelling, advanced mathematical/statistical models such as bootstrap and jackknife sampling to provide more accurate results for more accurate approximation for mean. median, variances, percentiles and standard deviation of big data.   Apache Hadoop  plays an essential role with projects such as  MapReduce, HDFS, HSQL (Hive SQL) and HBase. New central monitoring tools should be developed and embedded within the Big EDW to handle big data metadata such as social media sources, text analysis, sensor analysis, search ranking, etc.  Parallel Machine Learning and Data Mining, being looked at recently via projects such as Apache Mahout and Hadoop-ML combined with Complex Event Processing (CEP), amongst faster SDLC and project methodologies such as agile scrum for handling the Big EDW life cycle are also becoming standard in the realm of Big EDW.

Note that the phrase “Big EDW”  is not used anywhere else and is the naming that I thought could fit EDW growth in to a system that can also accommodate and manage  Big Data!

Enterprise Resource Management (ERP) systems are  organisational platforms for coordination of organisational processes and supporting data in order to provide cohesive and timely services by providing integration of HR, Finance, Manufacturing, Supply Chain and Customer Services as core activities. These could be extended further to contain other entities such as Project Management, Asset and Maintenance Management, etc. ERP systems normally are supported by a Relational Database Management System (RDBMS).

Business Intelligence Systems are created to provide timely decision making power for organisations. More and more organisations use Business Intelligence to gain ability to access the correct information in the format that is easy to understand and analyse, or even in the form of applications providing answers to specific business queries.

As both ERP and BI systems are complex, supporting vast number of business processes and related information, to model and communicate the relation between business processes in ERP systems and related  Business Intelligence Objects, an extension to OMG (Object Management Group) UML 2.o Activity Diagram  for Business Intelligence and Data Warehouse is suggested in the white paper “Extending UML 2 Activity Diagrams with Business Intelligence Objects” by Veronika Stefanov, Beate List, Birgit Korher. In this paper a BI profile is introduced by defining new object stereotypes “DataRepository”, “DataObject” and “PresentationObject”. “DataRepository” covers “OperationalDataStore”, “DataWarehouse” and “Datamart”. “DataObject” covers “Entity” and “Fact”, and finally “PresentationObject” covers “Report” and “InteactiveAnalysis”. To view the extended meta model see Fig 2.0 in the above paper.

With the corresponding notation for the extended stereotype, the profile is a powerful tool for modelling the BI Objects in UML 2.0 activity diagram.  As in the above diagram, the diamonds are notation for “Fact” Objects, representing multidimensional data models, showing “Customer” and “Policy Transactions”, as well as “insurance company” Data Warehouse.

I am currently reading a book by Ken Collier, called “Agile Analytics, A Value-Driven Approach to Business Intelligence and Data Warehousing”.

This book is specifically written for Agile BI and Data Warehouse projects and includes a BI project scenario for a factitious company called FlixBuster.

The book has two parts:

Part I is about Agile Management Methods and concentrates on  management of Agile BI projects and teams. This part covers topics such as User Stories for BI Systems and Self-Organising Teams Boost Performance.

Part II is about Agile Technical Methods for delivery of BI systems and how the team can drive business value by producing working BI/BW results often. These will include topics such as Design,  Test Driven Data Warehouse, Version Control and Project Automation.

An excellent and unique book for both BI/DW Project Managers,  Scrum Masters and the technical BI/DW teams such as ETL Professionals, DBAs and Source Data Specialists. Also great for companies who would want to run their own internal BI/DW Agile projects.

I end this brief introduction with a couple of quotes:

“A sweeping presentation of the fundamentals that will empower teams to deliver high-quality, high-value, working business intelligence systems far more quickly and cost effectively than traditional software development methods.” — Ralph Hughes, author of Agile Data Warehousing

“This book captures the fundamental strategies for successful business intelligence/analytics projects for the coming decade. Ken Collier has raised the bar for analytics practitioners—are you up to the challenge?” — Scott Ambler, Chief Methodologist for Agile and Lean, IBM Rational Founder, Agile Data Method

I have identified three categories  related to the nature of Business Intelligence projects that makes them highly suitable for Agile approach. These categories are Skills, Change,  and Data.

 Skills

* BI projects are cross organisational and require both business and IT skills.
* Skills required for a successful technical delivery of  BI projects vary from Data Architect and DBA to ETL and Output Developer.
* There is a high chance that the initial requirements are vaguely defined due to the unfamiliarity of subject domain experts with  BI capabilities.

Change 

*Changes to the business processes affect the BI requirements and due to the typical length of these projects, the change is inevitable.
* Upgrade or change of systems, infrastructure and underlying technology  affect the BI implementation and delivery.
* Change of people during the project affect the requirements due to variety of management and operational styles and level of skills.
* Market conditions, regulations and legislations and any other factor that affect the business and strategy in any shape or  form affects the BI requirements or its priorities.

Data

* BI projects rely on accessing and understanding data related information, i.e., metadata
* Master Data definition plays a crucial role in BI projects.
* Data Quality affects the speed of the implementation and delivery of the BI projects.

I consider Hadoop as one of the technologies that creates a  link between Big Data Analytics and  Business Intelligence . In my previous posts I explained what Big Data means and what was the meaning of Unstructured Data. In this post I would like to introduce Hadoop, which makes it possible to gain business value from the Big Data.

Apache Hadoop is an open source project, providing software for reliable and scalable distributed computing. A simple programming model provides the ability for the distributed  processing of large data sets.  This is achieved by using a cluster of distributed processing and storage and so make it possible for Hadoop to easily scale up as required. Hadoop consists of three subprojects: Hadoop Common, Hadoop Distributed Files System (HDFS) and finally Hadoop MapReduce. Hadoop ecosystem of products also include derived technologies that could be used on their own or together to achieved the desired outcomes. Some of these related projects are Hive, Hbase, Zookeeper, etc For more details on each of the above projects, please visit http://hadoop.apache.org/

Core Hadoop is HDFS and MapReduce.

HDFS is Hadoop Distributed File System and is used as a utility in Hadoop projects to distribute data blocks to nodes in cluster which results in extremely fast computation.

MapReduce is an algorithm that makes it possible to perform parallel computing across the nodes in a cluster.

For Business Intelligence, one of the Hadoop projects, called Hive, is a data warehouse system for Hadoop compatible file systems (such as Apache HDFS or Apache HBase) and allows query, analysis and creating summary of of big data using a specific query language called Hive-QL.

Data is growing faster than ever and at the moment it doubles every year!  This will become astronomical and out of hand soon as around 80% of this data is Unstructured Data. Projects like Apache Hadoop makes it possible to analyse the Big Data and related projects such as Hive will make equivalent data warehousing for further storage and analysis of relevant data.

The name “Unstructured Data” does not somehow define the type of data it refers to.

Generally when organisations use systems and applications, there is a database in the back end and  mainly in “Relational” format.  In a “Relational” database, data is designed to be saved in tables that relate to each other in a way that follow certain rules, called normal forms. This is a database design model that guarantees the users of the corresponding systems, such as ERP systems, to insert, amend and delete data in a quickest way. This is all about performance of applications and the related screens.

But normalised data in relational databases are not very good when the data is to be queried. To solve this problem, the relational data designers use indexes and other methods for querying and displaying the relational data, but use of so many indexes will reduce the performance of the system and so this is not an effective way when reports are required on historical data.

To solve this problem, data often is remodelled as dimensional and saved into another database, usually a data warehouse or a data mart.

All said the data saved in the systems and relational databases are a fraction of the information held in an organisation. Any data that is not saved into a relational or dimensional database, is referred to as “Unstructured Data”, despite the fact that these data may well have structures related to them!

Two examples of  unstructured data  that still have related structure are documents in file system and body of the emails. As certainly there is structure to file systems as well as data related to the information in body of emails, these data cannot be considered aas data with no structured, but still categorised as unstructured data. Other examples of unstructured data are Microsoft Office files, such as Word documents, Excel Spreadsheets, Visio Diagrams, pictures, scans, videos, webcasts, web data including social networks such as facebook and twitter, wikis, web blogs, and any text or picture data saved in any format such as logs.

Statistics shows that less than 20% of data in organisations are relational and so the remaining data is saved and kept outside a relational database and considered as unstructured data. The velocity of growth in unstructured data is faster and the variety and volume is also way higher than the relational data.

Up to now, it was physically impossible to use any sort of analysis on unstructured data due to its volume and variety. This issue is now becoming less of a problem, with new advanced methodologies in distributed computing.

In my next post, I will explain Apache Hadoop and how this can come to rescue to create amazing ways to analyse the “Big Data“.

For so many years many companies were concerned with the data  they didn’t have. The main worry was how to collect the required data. Many applications and systems were developed to give organisations the required entity for gathering the data.

With collection of data growing in many systems and applications, the companies then faced other challenges, such as how to make sense of the data they had and turn them to actionable insights. Data Services and Integrations along with Business Analytic and Performance Management Tools then came to rescue.

In the last two years, companies started to realise the volume, variety and velocity of data growth. In fact 90% of the data in the word today has been created in the last two years!

Volume: These days an enterprise will have a  petabytes (1 million gigabytes) of  information and it is growing.  This is Big data!

Variety: Big data is not just the data stored in  relational databases. It includes unstructured data in all documents, audio, video, live web data such as in wikia, blogs,  tweets, facebook, etc.

Velocity: The speed in which Big data is produced, makes it absolutely necessary to be analysed for insight as near as  it happens, i.e. near live. There is no  time to wait for later analysis.

Business Analytic and Intelligence is growing into Big Data space as we speak.