Big Data – An Infographic Perspective

September 3, 2012

CSC is one of the pioneers in the rapidly growing field of big data.As most of us already know, ”big data” is changing dramatically right before our eyes – from the amount of data being produced to the way in which it’s structured (or not) and used. One million time as much data is lost each day than is consumed. This trend of big data growth presents enormous challenges, but it also presents incredible business opportunities (Monetization of Data). This big data growth infographic helps you visualize some of the latest trends.

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Business Intelligence Trends

August 29, 2012

There is no doubt that 2012 will be another big year for BI and information management. In the article we`ve tried to gather what we suppose are the top BI trends for near future

Big Data → Need for Speed

The rise in volume (amount of data), velocity (speed of data) and variety (range of data) gives way to new architectures that no longer only collect and store but actually use data: on-demand or real-time BI architectures will replaces traditional datawarehouses. Successful business intelligence projects will need to consider Big Data as part of their data landscape for the value that it delivers. More and more organizations will look toward statistics and data mining to set strategic direction and gain greater insights to stay ahead of the pack.At the same time the BI user is expecting faster answers from their BI environment disregarding the fact that the size of data is increasing.

Shift from analytical BI to operational BI

Increased adoption of cloud and mobile BI encourage individuals to access their KPI dashboards (key performance indicators), more often. An operational dashboard works much like a car’s dashboard. As you drive, you monitor metrics that indicate the current performance of your vehicle and make adjustments accordingly. When the speed-limit changes, you check your speedometer and slow down, or when you see you are out of gas you pull over and fill-up. Likewise, an operational dashboard allows you to make tactical decisions based on current performance, whether it is chasing a red-hot lead or ordering an out-of-stock product.

Data democracy

Latest surveys showed that only 25% of employees in businesses that adopted BI had access to that tool. And that is not because they didn`t want to or didn`t need information, but because traditional BI tools have been too bulky and technical for that other 75% of employees to use.
As now organizations more and more are adopting cloud and mobile BI dashboards, this situation is likely to change. Business intelligence is heading towards simpler, more straightforward methods and tools..

Agile

An Agile approach can be used to incrementally remove operational costs and if deployed correctly, can return great benefits to any organization. Agile provides a streamlined framework for building business intelligence/data warehousing (BIDW) applications that regularly delivers faster results using just a quarter of the developer hours of a traditional waterfall approach.

It allows you to start a project after doing 20 per cent of the requirements and design that deliver 80 per cent of the project’s value. The remaining details are filled in once development is underway and everyone has a good look at what the challenges actually are.

BI going mobile

In a survey conducted by Gartner, it was found that by 2013 one-third of all BI usage will be on a mobile device, such as a smart-phone or tablet. BI users want to access their data anytime and anywhere. This puts a demand on both the backend of any BI solution (like datawarehouse appliances) but also on the frontend where information access and visualization must be possible.

BI going up to the Cloud

As Cloud computing continues to dominate the whole IT landscape, so BI also dominates in the Cloud . Throughout next few years adoption of cloud BI tools will be driven by a number of important factors. First, cloud-based solutions offer the advantage of being relatively simple and convenient to deploy. Second, cloud tools are more easily scalable to provide access to key performance indicators (KPIs) to everyone in your organization, no matter where they are or what device they are using. Lastly, continually improving security measures will put to rest any reservations businesses have with storing their sensitive data in the cloud.

We believe these above enumerated areas will grow over the next few years. Organizations will embrace the Agile approach, utilizing new tools and technologies to decrease delivery times and demonstrate substantial business value. As we put more data into the Cloud, big data will become standard. Data itself will be delivered to satisfy the desires of users, so access from mobile devices will dominate desk-based consumption. The businesses that embrace these new business intelligence trends, and take steps to change and adapt the way data is hosted, analyzed, utilized and delivered, will be the ones that grow and prosper in the near future.

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Bi24 covers all of Gartner’s predictions

July 13, 2012

Gartner’s 2012 predictions for business intelligence focus on the challenges around Cloud, alignment with business metrics and a balanced organisational model between centralised and scattered.  CIO Australia has highlight the top six BI trends for the year ahead are:

  1. BI in the Cloud
  2. Mobile BI
  3. Analytics
  4. In-memory analytics
  5. The Agile approach to BI
  6. Big Data

The above is really big news for Bi24, C24′s cutting edge business intelligence solutions, as we cover all these areas in one solution. Current uptake of the product has seen clients throughout the UK and Europe using the solution to drive business. For more information please visit www.c24.co.uk

 

See CIO.COM

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COMPETING ON ANALYTICS: AN ARTICLE REVIEW

July 9, 2012

A Harvard Business Review Article by Thomas H. Davenport, Article Review by Akhmad Rahadian Hutomo

Since the late of 1990s, the term business intelligence (BI) and its application has been widely known and used in organizations, especially in large enterprises. But in a decade later, they started to realize that changing business environment will needs something more than just BI, which now called business analytics. In 2006, an author named Thomas wrote an article on HBR entitled “Competing on Analytics” which provisions the rising needs for business analytics. Davenport started his explanation on competing analytics by giving some examples on the succesfull usage of killer apps in some organizations, named Amazon, Harrah’s, Capital One and Boston Red Sox. By utilizing analytics, these organizations are able to knows better about the values that customer want, which inturn be able to squeeze all the value from the processes and make the best out of it. Davenport also point out that, to be an analytics competitor, top-down approach from the senior leadership team, as well as hiring the best people are necessary. Nonetheless, not all organizations are succesfull on using business analytics due to its characteristic. The rest of the articles explains about what organizations can make the best of analytics, as well as the changes that an organization must undergo to adopt it.

ANATOMY OF AN ANALYTICS COMPETITOR: MUST-HAVE CHARACTERISTICS FOR ORGANIZATIONS

Some traditional organizations may not be fully suitable with competing analytics. One best practice that an organization my want to know is how Marriot International using analytics. But, it will not work to some traditional organizations. Davenport’s study found three key attributes that an organization must have:

WIDESPREAD USE OF MODELLING AND OPTIMIZATION

Analytics competitors do things beyond statistics and spreadsheets. They are using sort of things that could provide them better insights from data, such as:

  • Predictive modelling to identify the most profitable customer.
  • Data warehouse to pool inhous and outside data.
  • Optimized supply chain.
  • Real-time pricing.
  • Sophisticated experiments to calculate impact.

Some analytics competitors, especially inscurance company, like Capital One and Progessive doing series comprehensive experiments to have the best value based on their customers need, even with high-risk.

AN ENTERPRISE APPROACH

Successfull analytics competitor will implement analytics using multiple applications in wide busines functions rather than using single app. For some companies such as UPS, Capital One and Barclays Bank are already implementing business intelligence and then shifting towards full-bore analytics competitors. However, Devenport thinks that BI still have some flaws where its still use data which spreads all over the organization. The data may contains errors and make the decision inacurrate, which in contrast, analytics competitors are using centralized function to manage critical data. People within the organization is as important as the technology. Some organization like P&G create a pool of experts from various function to do the analytics.

SENIOR EXCECUTIVE ADVOCATES

Changing into an analytics competitor simply changes the organization, and it will require leadership skills to guide the organization towards sucessfull adoption. Its proven that if the initiative just pushed by one-or-two business unit leaders, it will not successfull. There was some key leadership qualities that the article pointed out, such as: appreciation and familiarity with analytics or analytics-minded, intuitive, and have the guts to make decision even not supported by numbers.

THEIR SOURCES OF STRENGTH: WHAT MAKES AN ANALYTICS COMPETITOR RUNS

Basically Davenport define 4 things that makes an analytics competitor ticks, they are:

THE RIGHT FOCUS: HAVING A CLEAR SIGHT

Even if an organization have the ability, it is necessary to have certain focus on only a few analytics subjects. Becoming to diffuse can make the organization losing clear sight on the purpose of analytics. Another consideration of focus is about having a deep analysis on at least 7 functions. Nowadays, advanced statistic models and algorithm ca be used widely, including in advertising and other marketing measures. Later on this subtopic, there are examples that sucessfull analytics competitors can’t be done by the organization alone, it also needs to help their vendors and customers.

THE RIGHT CULTURE: TO JUSTIFY EVERYTHING QUICKLY

The right culture to have is the culture to appreciate usage of data, fact and the things between that and the procedure to get it. It also applied in organization with high creativity and intrapreneurship: any innovation should be made based on evidence. However, always justify everything also have payoff: it might be taking long time and costly, so the managers hould balance them in order to make quick decisions.

THE RIGHT PEOPLE: THE BEST OF THEM

Analytics competitors hires best people on analytics, bunch of them, to do the analytic-based decisions and make it seamlessly in line with the business. But, the people to do the analytics just as good as how far they can communicate it, so they must have sort of good interpersonal skills. In terms of formula, it might look like this:

Good Analyst = Expertise +Ablity to express it in simple way + Interpersonal skills

Of course, to get people with this quality is not easy, not to mention taking long waiting time. To have an overseas employee might be a good idea.

THE RIGHT TECHNOLOGY: THREE PILLARS

Analytics and IT are unseparable. It is supported by three pillars: First, THE DATA,whether it is from ERP, CRM, POS, any of them, and a lot of them, means years of data. They put it in data warehouse, which a familiar tools on BI. Second, THE BI SOFTWARE, to collect data from warehouses, analyse them and making reports. And Last, THE COMPUTING HARDWARE which enables a computation power for huge volume of data, quickly.

THE (LONG) ROAD AHEAD

Well, it might be not long road, as Davenport writhe the articles 5 years before this review written in the late 2011. He was concluding his paper with reminding us that to become an analytics competitor will takes a long time until the ROI, while meantime, it will cost many efforts and expenses. Yet, it can be done gradually from current time by collecting data and refining the system, and equip the organization with analytics-minded people.

COMMENTARY

Business analytics might be an interestring concept to explore to enrich our current knowledge and view on today’s business intelligence. In contrast with BI, business analytics focuses on gaining insights and overview of organizational performance based on data and statistical methods, supported by BI applications. It also cover the issues of leadership, culture and having a certain quality of analyst within the organization. On the article, Davenport gives the readers a comprehensive look of business analytics without losing the big picture. His writing also well supported with examples which gives personal and easy-to-digest touch on complex concept. A worth to read for BI enthusiasts.

Based on a Harvard Business Review Article Titled “Competing on Analytics” by Thomas H. Davenport Published on January 2006, Article Review By Akhmad Rahadian Hutomo for Business Intelligence Assignment, Information System, Faculty of Computer Science, Universitas Indonesia on October 2011.

http://ianhutomo.wordpress.com/2012/07/07/business-intelligence-in-human-capital-driven-companies/

 

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Reflections on #ACM Webinar “2012 – #BigData: End of the World or End of #BI?”

July 3, 2012

Some notes on the ACM Webinar on 2012 – Big Data: End of the World or End of BI? by Dr. Barry Devlin of 9sight Consulting

The original Data Warehouse Architecture was conceived in 1988 as a single logical storehouse. This changed in the early 1990′s into a layered model of an Enterprise Data Warehouse with Data Marts.

There are four (4) ancient postulates of data warehousing:

  • Postulate 1 (1970s): Operational and informational environments should be separated for both business and technical reasons.
  • Postulate 2 (1980s): A data warehouse is the only way to obtain a dependable, integrated view of the business.
  • Postulate 3 (1980s): The data warehouse is the only possible instantiation of the full enterprise data model.
  • Postulate 4 (1990s): A layered data warehouse is necessary for speedy and reliable query performance.

See: Devlin, B. “Business Integrated Insight (BI2): Reinventing enterprise information management”, (2009), www.9sight.com/resources.htm

Slide #8

This explication of the underlying assumptions (or postulates) helps to explain the evolution of the data warehouse architecture. It seems now that these decisions were made based on the available computing power at the time. The operational data stores were straining under the load at the time, and BI was seen as a luxury compared to the real business of making money. Now with the large computing resources of CPU, disk space, and networks, this constraint is no longer a barrier to integration of front-end and back-end business processes.

Devlin says that the explosion in the number of DW components from the mid 1990s onwards suggests that the data warehouse architecture is failing. From my perspective, this mess came about because some enterprises tried to do data warehousing on the cheap. Requirements were usually vague and the implemented solutions were ad-hoc. I think Devlin is saying that this mess was inevitable given the ancient posulates given above.

After reflecting on this mess, Devlin came up with five (5) modern postulates for highly evolved business:

  1. Modern business processes seamlessly combine action-taking and decision-making, and require an integrated continuum of consistent information.
  2. The new information architecture must be based on a comprehensive enterprise information model, spanning all types of information used in the business.
  3. The business information resource is best maintained as a single copy of each data item, with only the most minimal resort to transient layers or copies of specific subsets of data for specialized needs.
  4. An integrated, model-based and closed-loop process environment is needed to create, maintain and use both the business information an activities.
  5. An integrated, flexible and role-based user interface provides access to the entire business information.

Slide #10

What is a comprehensive enterprise information model? How is it different from a data model? Data model was mentioned in postulate #3 above. So, are we moving up the knowledge hierarchy from data to information? If so, I think the analysis is confused by the ambivalent meaning of data model—see my earlier notes at On the Logical Difference Between Model and Implementation.

Devlin goes on to propose a new architecture Business Integrated Insight (BI2)…covering all information and process:

  • People Personal Action Domain
  • Process Business Function Assembly
  • Information Business Information Resource

See: Devlin, B. “Business Integrated Insight (BI2): Reinventing enterprise information management”, (2009), http://bit.ly/BI2_White_Paper

Slide #11

Devlin introduces Biz-Tech ecosystem. He does not think IT is dead despite what many analysts say. He says that IT has evolved into a Biz-Tech ecosystem which is the fully symbiotic existence and IT. This has the following three (3) characteristics:

  1. Interdependence
    New technology enables business possibilities;
    new business opportunities drive technology advances
  2. Reintegration
    Silos in business and IT are obvious to Web-savvy customers;
    coherence becomes mandatory
  3. Cross-over
    Business people need IT skills to see how to recreate the business with new technoology;
    IT people need business acumen to see how to satisfy business needs in new ways with emerging technology

Slide #14

This view flies in the face of the idea of computing (or IT) as a commodity. IT people need to be integrated into the business as much as sales, marketing, HR, production, and design. All of these people has to come together to create a coherent product for the customer. IT people are no longer resources simply to be brought on the open market. And IT people need to stop thinking of themselves as simply Java programmers or Oracle DBAs.

He gives three (3) examples of Biz-Tech ecosystems:

  1. Business Intelligence reinvents Retail (cf Walmart)
  2. The web recreates the library (cf Wikipedia)
  3. Big data redefines automobile insurance—Pay as you drive

Devlin sees evolution of BI2 occurring in three (3) parts:

  1. Removal of layers in BI2.
    • Introduction of the advanced information warehouse which has pillars rather layers. Data, metadata, and models are shared across the pillars. EDW has evolved into Core Business Data. (See slide #21)
    • Data virtualisation becomes more important by enabling queries to be constructed across differing data stores.
  2. Dealing with new information types:
    • Big data challenges our fundamental beliefs about the relationship between data and knowledge.
    • The DIKW pyramid is no longer valid. (Date -> Information -> Knowledge -> Wisdom) (See slide #23)
  3. Introducing m3 – the modern meaning model (see slide #24)
    • Decision making moves from individual to collaboration
    • Decisions are not rational

Devlin gave the following picture of the Modern Meaning Model:

Devlin's Modern Meaning Model (m3)

I have not absorbed this model yet, but it does appear to be sensible. Whether or not it is useful remains to be seen.

Devlin sees mobile computing as important as the producer and consumer of information, and decisive in team-based decision making (the iSight Model—see slide #29). He sees the informal interactions being recorded for future analysis.

Devlin’s conclusions are:

  1. Overall—simplify the BI environment
    • Less layers, less copies, less ETL
    • Recognise the emerging biz-tech ecosystem
  2. Big Data—forget the hype, but do evaluate
    • Business opportunities may exist in unexpected places
    • Recall that big data has very different characteristics
  3. Enable innovation through team working
    • Collaborative decisioning vs. collaborative BI
    • The emerging role of informal information

Slide #31

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More Data, More Problems? Enterprise Data Protection in the Era of Big Data

July 2, 2012

The explosion of data available today has been both a blessing and a curse to enterprises in all verticals. The ability to collect, store, mine, and analyze huge quantities of data has changed the way that companies do business, providing a competitive advantage to those companies that can best leverage their big data. According to a report by Mckinsey Global Institute, “a retailer using big data to the full could increase its operating margin by more than 60 percent.” Such an advantage is hard to ignore. Yet the increased storage and use of this data increases the complexity associated with securing that data.

As concerns around data security grow apace with the adoption of big data mentality, some companies struggle to find the balance between collecting enough data to compete and ensuring that their business is not threatened by the likelihood of a compromise. Data protection remains a vitally important element. In fact as more data is collected and stored, data protection should become a more prominent concern for enterprises.

Big data can contain many different categories of sensitive data – customer data, corporate information, and even intellectual property. The vast majority of the data is in semi-structured or unstructured format. Both the quantity and the structure of the data bring with it concerns about security and close on its heels, performance. However, performance doesn’t need to be an issue when considering theencryption of big data. Technological innovations, such as IBM’s AES-NI, can help companies have their data and use it, too.

 

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Lost in the Cloud – Are Businesses Really in Control of Their Data?

June 28, 2012

Managing and protecting corporate data is a major challenge. As the technology evolves, so must our data protection strategies. Unfortunately, as our March 2012 report on “The State of Data Protection” revealed, most organizations aren’t confident about their data protection practices: 80% of respondents said that they store data belonging to customers, vendors, and other business partners, but only 26% were very confident that the data was protected.

Now, with cloud adoption ramping up, IT is charged with solving a whole new set of data protection problems. Which data should go to the cloud and which data should stay? How do I enforce this? How do I provision and manage access to cloud services? How do I prevent everyone from using their own favorite solution in favor of company sanctioned ones? The list goes on.

To see the effect of cloud services on data protection, Varonis recently surveyed IT workers from over 400 organizations to gauge their adoption of cloud-based collaboration, and their perception of its security. The results indicate that organizations need to formulate their data protection strategy for cloud collaboration now– the controls gaps present with cloud-collaboration in the mix are reminiscent of the gaps reported by those that were “not confident at all“ that their data was secure in our data protection survey. Organizations may well be under pressure to better control the data that makes its way into the cloud.

How bad is it? Here is a sneak preview. Be sure to download the full research report here for an in-depth look at IT’s view of cloud adoption.

Enjoy, share, embed our infographic!

Lost in the Cloud - Are Businesses Really in Control of Their Data?

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Big Data, it is all about it at the moment

June 18, 2012

The IT industry has a penchant for inventing new buzz words for topics that have been around for years in one form or another and perhaps Big Data is another example.

Yet just this week Capgemini announced the findings of a report (“The Deciding Factor: Big Data & Decision Making”) which showed that, in a study of over 600 C-Level execs, 9 out of 10 leaders believe data is as fundamental to their business as people and capital.

With the amount of data being generated reaching astronomical levels (and accelerating) buzz word or not, Big Data is a problem all business leaders need a strategy for.

Ever wondered just how much information is created? Domo produced an eye-opening infographic which you might be interested in.

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The C-level is coming around to big data (infographic)

June 12, 2012

Click to visit the original post

According to a new survey by the Economist Intelligence Unit (commissioned by IT consulting giant Capgemini), corporate executives are starting to figure out that big data matters and how to leverage it, even if they haven’t fully come around on the concept.

The surveyors questioned more than 600 C-level and other senior executives across the globe, finding that while they understand certain realities — such as the importance of valuable analysis versus sheer volume of data, and the increasing role of data to inform intuition — most respondents (55 percent) still don’t think their management teams view big data strategically enough.

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10 Things IT Should Be Doing (But Isn’t): Free On-Demand Webinar

April 4, 2012

On our last webinar: 10 Things IT Should Be Doing (But Isn’t), we reviewed some of the challenges associated with unstructured data management and protection. IT requires the ability to answer critical questions about data in order to efficiently and effectively protect it. Some of these questions are:

  • Who has access to data?
  • Who has been accessing data?
  • Where is my sensitive data over exposed?
  • How do I fix exposures?

During the webinar we gave an overview of 10 things IT should be doing to answer these and other fundamental questions, and put the answers to productive use. Maintaining a complete audit trail of access activity, an accurate map of permissions, and identifying data owners are a few of the things IT should be doing. We reviewed why each one of the 10 things is important and what to look for in an automated solution.

If you missed our webinar, https://varonis.webex.com/varonis/lsr.php?AT=pb&SP=EC&rID=26300867&rKey=eac45ec0eefae25e to play the recording.

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