Data Retention in the Social Media Era

April 11, 2013

A variety of industry research analystshave indicated that 3 of the top 10 priorities for IT in 2013 will be initiatives focusing on BYOD, cloud computing and business analytics obtained via Social Media.  While these initiatives provide clear business benefits, they will challenge data retention and records management policies for most organizations.

BYOD, cloud computing and social media have a common thread – they all create data repositories that have been geared towards the non-IT consumer, where governance, management and retention have taken a backseat to ease of use.  With the introduction of these technologies into the enterprise, companies are obligated to develop backup, archiving, and classification strategies to ensure that relevant data is available in the event of litigation and a discovery request.

The Federal Rules of Civil Procedure state that the moment a company receives a legal hold request they must not dispose of data without having a clearly defined and demonstrable retention and disposal policy. These policies cannot be developed and implemented in the midst of litigation as an opposing  litigant could claim that destruction of data was intentional, resulting in damages and penalties awarded to the opposition.

In the article, eDiscovery Rules Applied to Social Media: What This Means in Practical Terms for Businesses, statistics show that the FRCP rules are being enforced— sanctions were ordered in 50% of the cases where sanctions were sought, with a few resulting in large monetary penalties. Needless to say, companies are compelled to comply.

While many companies have chosen the pack-rat approach – save and archive all of the data they manage, including customer data, personal data, etc., this approach is not practical due to everincreasing volumes of data, especially when considering the information generated by mobile devices and social media.

In the event that a company does need to develop a defined retention policy that takes these initiatives into account, their requirements should be part of a larger blueprint for securing their data, linking their retention strategies with governance and accessibility.  These 6 steps provide some basic guidelines:

  1.  Determine the age at which each type of data that has not been accessed would be considered stale – 1 year?  2 years? 5 years?
  2. Implement a solution that can identify where stale data is located based on actual usage (not just file timestamps)
  3. Automate the classification of data based on content, activity, accessibility, data sensitivity and data owner involvement
  4. Automatically archive or delete data that is meets your retention guidelines
  5. Automatically migrate data that is stale but contains sensitive information to a secure folder or archive with access limited to only those people who need to have access (e.g. the General Counsel)
  6. Make sure your solution can provide evidence (e.g. reports) of your defensible data retention and disposal policy

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IBM IOD 2012 – How Analytics is Transforming the C-Suite.

December 12, 2012

Fred Balboni, Global Leader, Business Analytics and Optimization, speaking to the need of infusing analytics throughout the organization and how IT and LOB executives are changing partnership models to bring this to reality. In the panel discussion, JP Morgan Chase shared how they are using analytics to mine information from the “new” customer who is banking via mobile channels and Thompson Reuters described the role that analytics plays in their customer centricity by creating upsell and cross-sell opportunities externally and greatly reducing the cost of ownership internally.

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The 4 Key Requirements for Business Intelligence Reporting

September 26, 2012

A recent white paper published by Birst, Inc., a San Francisco based provider of “agile business analytics” software and solutions, points up the four ‘foundational requirements’ of a business intelligence (commonly called “BI”) solution.  They remind us that our ERP systems are merely a tool, a means to an end, and that end is to extract intelligent information from the underlying data in order to improve our business management decisions.

The article, available here (you’ll have to provide contact info first) points to four key capabilities (along with our own commentary about them):

1.) Historical analysis and reporting.  You want information not just on your business performance, but on the key drivers of that performance as well.  You need to know not just your results, but your influencers.  This usually involves mapping and understanding data over a long time frame, measured often in years.  That’s a lot of data.

2.) Forecasting and future projection.  Collecting and understanding your data is one side of the task.  Projecting into the future is the other.  So for example, once you know something about the progress and flow of past sales deals, the size of your pipeline, the length to close… you’re more able to project the progress of future deals.  The goal is to align your resources with your forecast for maximum efficiency.

3.) Ability to integrate information from multiple business functions.  Integrating the data you need to make better decisions may require multiple data sources.  Obviously, this burden is minimized if you’re operating under, more or less, a single (or limited) silo of information.  This is where an integrated ERP solution starts to really shine.  Often the data there, give or take the contents of a couple of spreadsheets, is more than enough to provide meaningful insight.

4.) Easily explored reporting and analysis.  Decision makers need to understand the big picture.  Sometimes, they need a good bit of detail to be able to do so.  This speaks to the need for explorable reports, drill down capabilities, ad hoc queries and business dashboards.  Flexibility and robustness, without being overly complex, are helpful.  Today we find the better ERP systems can provide much of this.  More sophisticated BI solutions will boost your reporting capabilities significantly, a feature most appreciated in larger, more diverse organizations.

A solution that provides the above foundation, whether it’s part of an ERP system or an add-in, ensures you’ll have the right analytical tool when it comes time to convert hard data into meaningful information that can inform better decision making.

Ironically Bi24 provides all these elements and much more

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CIOs Need to Make Information Management a Real Priority

August 9, 2012

 

Fantastic article from Ventana Reseacrh

Our recent benchmark research on information management uncovered some startling facts about the level of technology adoption necessary for efficient information-centric organizations. Chief information officers (CIO) are responsible for the availability of information to their businesses in a consistent and timely basis, but in most organizations, information management is seen as just a delegated set of tasks and is not the CIO’s top priority. This unfortunate outlook can have a lasting impact on the efficiency and profitability of a business.

Our business analytics benchmark of more than 2,800 organizations found that two-thirds spend the majority of their time on data-related tasks rather than analytic ones. Analysts spend too much time using tools such as Microsoft Excel to copy and paste the data they need to communicate to meet information requests. Lack of availability and lack of consistency in a company’s information has a severe negative impact on its business analytics.

Our benchmark research on information management found some opportunities for businesses to gain ground. Almost two-thirds (63%) have confidence in their organizations, saying they have the right team to improve information availability, but at the same time they admit that data spread across too many applications and systems (67%) and multiple versions of the truth (64%) are barriers to information management. Getting the right accurate information still plagues most organizations, and most do not have a plan to improve this situation. While organizations have complained for decades about lack of access or accuracy of the data, today the impact of these issues is better known. Only 19 percent of organizations indicate business and IT work well together, while 56 percent say they work fairly well together and are focused on improving. Most organizations still operate in silos and talk about working together more than they actually do.

Our research found a lack of adoption of key initiatives to help manage information assets more effectively. Even key initiatives that are completed, from master data management (MDM) (10%) to data virtualization, data quality, data integration and data governance (16%) are employed by just a fraction of organizations that should be mastering the science of information management. We saw some potential for improvement; initiatives in data integration and data quality are in effect at 28 percent of organizations, but in other areas the number was smaller. The majority of initiatives focus on customer-centric data: The number ranges from almost three-quarters of some organizations to much less in financial, employee, product and supplier data. These data-related initiatives are critical for organizations that need to deliver information management. An organization that does not have them completed and working together is taking significant business and financial risk by running at an unacceptably low level of efficiency and accuracy.

Information management in a distributed enterprise environment is no easy task when you need a common information warehouse. We found that too many incompatible tools (57%) and many unsynchronized metadata stores (42%) were the top two obstacles to having a common information warehouse.

IT management puts itself in a difficult situation when it fails to invest in resources and technology to improve information asset management. While projects are being initiated and planned, in many cases data availability is not being improved fast enough to meet business needs. We found the largest obstacles were insufficient staffing (68%), inadequate budget (63%) and insufficient training and skills (59%), which means that many organizations are ignoring this issue or operating with less than skilled resources that are already overstretched.

This has to change, and our benchmark found a lot of potential places for improvement. CIOs need to create a strategic plan for information management to ensure they are focused on the factors necessary to equip their information architecture to meet business needs. Unfortunately, even as organizations begin to see the importance of information management, we have the current fixation on handling big data, which takes away resources that could be devoted to getting information management efforts in order. The reality is that big data does not operate efficiently without an efficient information management environment. Just adding another data source that is not well-integrated inevitably increases costs and uses more resources.

Your next step should be to make information management a strategic top agenda item for your CIO. Other priorities, including business analytics, business applications and big data, will not reach their full potential without top-notch information management that integrates business and IT efforts.

 

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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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Bloomberg on State of Business Analytics

April 12, 2012

 

by Ravi Kalakota

Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?

According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.

While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.

This is a far cry from the current hype around analytics and big data, raising the questions:

  • How should an organization be structured to effectively leverage analytics?
  • What skillset, mindset, toolset adjustments are needed to “think outside of the box”?

These are questions that managers must ponder as they rampup investments. Many companies start their analytics journey by executing one or two projects of small scope.

That may be fine at the outset, but in order to address the larger performance improvement issues, companies need to move up the maturity curve from repeatable to defined and then to managed and optimized.

The following are research insights highlighted by the survey sample of 930:

  • Business analytics is still in the “emerging stage.” While analytics has gone mainstream, most organizations still rely on traditional technology. Spreadsheets are the number-one tool used for business analytics.
  • Enterprises – small, mid, large, mega — have been collecting tons of data. They are dying to get more insights from it because it’s too much of a pain to extract anything from the databases.
  • Organizations are proceeding cautiously in their adoption of analytics. Use of business analytics within companies has grown over the past year, but at a moderate rate. Analytics also tend to be used narrowly within departments or business units, not integrated across the organization.
  • Intuition based on business experience is still the driving factor in decision-making. Analytics is used as part of the decision process at varying levels, depending on the organization.
  • Companies are looking to analytics to solve big issues, with the primary focus on money: reducing costs, improving the bottom line, and managing risks.
  • Data is the number-one challenge in the adoption or use of business analytics. Companies continue to struggle with data accuracy, consistency, and even access.
  • Many organizations lack the proper analytical talent. Businesses that struggle with making good use of analytics often don’t know how to apply the results.
  • Culture plays a critical role in the effective use of business analytics. Companies that have built an “analytics culture” are reaping the benefits of their analytics investments.

Nothing earth shattering here….Like all innovation, adoption will take time and require significant organizational changes across toolsets, skillsets and mindsets. But make no mistake, companies that don’t embrace analytics in a fast paced competitive environment will be left behind. Take for instance Financial Services industry. The sector continues to undergo massive structural change due to de-risking, ongoing regulatory changes (e.g. Dodd-Frank act, Basel 3), curbs on leverage, competition to cash-cows like credit-cards and a massive shift to online banking. This is driving skyrocketing demand for predictive models and creating an unprecedented need for data agility.

What Is Your “Analytics Maturity ”?

In order to change, you have to baseline first – what is your analytics maturity. The business analytics maturity curve represents the arc of progression every company moves along. Maturity levels are measured by your level of experience, the implementation and support strategies you use, and your degree of sophistication around data.

Analytics maturity can be assigned to one of the following four groups:

  1. Reactive businesses engage in business analytics only in a reactionary mode, e.g., by complying with a customer request or in response to competitive pressure.
  2. Responsive companies are engaging in business analytics, but mostly as separate, one-off projects.
  3. Proactive organizations have established processes, infrastructure, and resources to support business analytics in a programmatic manner.
  4. Aggressive companies aggressively expand analytics capability as an important growth opportunity and encourage their customers to adopt it.

Which type of organization do you belong to? Where do you want to be?

Notes and References

Source: Bloomberg Businessweek Research Services study, conducted among 930 businesses across the globe in various industries. Focus of the study is to provide insight into the current state of business analytics in today’s organizations. Also examine the challenges companies face when using analytics, and explore tactics favored by companies who have succeeded in using analytics more effectively than their peers.

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ROI on Analytics – Now We Have Numbers

March 6, 2012

by Shirish Netke

A recent study by the Nucleus Research says that Analytics pays back $10.66 for every dollar spent. The study is based on data from 60 case studies and relates to investments in Business Intelligence, Performance Management and predictive analytics. Not surprising are the areas where they saw ROI increase – revenue, gross margin and expenses.

Enterprises have used various metrics to track the effectiveness of Business Analytics. Cycle Time to Information (CTI) is a metric that measures the elapsed time between the occurrence of a significant event and the time this information is available to a decision maker who has to act on that information. Cycle Time to Action (CTA) is variation of this metric which measures the elapsed time to act on information after an event occurs.  These metrics are useful to track the efficiency of a Business Analytics infrastructure and the elimination of manual processes to increase productivity. As the volume of data increases in an enterprise, automation in data management will become more complex in the future.

The primary purpose of Business Analytics is to improve the quality of decision-making. Better decisions directly impact the business. Target, a hundred year old retailer, is using Predictive Analytics to expect shopper behavior (See Target Your Shoppers – Retail Predictive Analytics). Concept One, a manufacturer of apparel and accessories,  has used analytics to be more selective about renewing their licensing agreements.  Procter & Gamble is increasing their analytics staff fourfold while reducing IT spend in other areas (See Proctor & Gamble – Business Sphere and Decision Cockpits).

Yet, Business Analytics adoption in enterprises has not reached its potential. A IBM/MIT study in 2010 cited that the most common barrier to implementing an analytics solution are lack of understanding of how to use analytics to improve the business. Time spent on analytics competes with other priorities for business users.

An ROI analysis is a very useful tool for business managers who are trying to allocate scarce resources to get the biggest bang for the buck. Now they have something to talk to CFO.

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