You’ve done enough research to know that data management is an important first step in dealing with big data or starting any analytics project. But you’re not too proud to admit that you’re still confused about the differences between master data management and data federation. Or maybe you know these terms by heart. And you feel like you’ve been explaining them to your boss or your business units over and over again.

Either way, we’ve created the primer you’ve been looking for. just post it to the team bulletin board, or share it . And remember, a data management strategy should never focus on just one of these areas. You need to consider them all.

Data Quality

What is it? Data quality is the practice of making sure data is accurate and usable for its intended purpose. Just like ISO 9000 quality management in manufacturing, data quality should be leveraged at every step of a data management process.

This starts from the moment data is accessed, through various integration points with other data, and even includes the point before it is published, reported on or referenced at another destination.

Why is it important? It is quite easy to store data, but what is the value of that data if it is incorrect or unusable?

A simple example is a file with the text “153 MAIN ST Anytown, AZ 12345678” in it. Any computer can store this information and provide it to a user, but without help, it can’t determine that this record is an address, which part of the address is the state, or whether mail sent to the address will even get there. Correcting a simple, single record manually is easy, but just try to perform this process for hundreds, thousands or even millions of records!

It’s much faster to use a data quality solution that can standardize, parse and verify in an automated, consistent way. By doing so at every step, risks like sending mail to a customer’s incorrect address can be eliminated.

Data Integration

What is it? Once you have accessed the data, what do you do with it? A pretty common next step is to combine it with other data to present the unified results.

Data integration is the process that defines the steps to do this, and data integration tools help you design and automate the steps that do this work. The most common types of data integration tools are known as ETL, which stands for extract, transform and load, and ELT, which stands for extract, load and transform.

Today, data integration isn’t limited to movements between databases. With the availability of in-memory servers, you might be loading data straight into memory, which bypasses the traditional database altogether.

Why is it important? Data integration is what allows organizations to create blended combinations of data that are ultimately more useful for making decisions.

For example, one set of data might include a list of all customer names and their addresses. Another set of data might be a list of online activity and the customer names. By itself, each set of data is relevant and can tell you something important. But when you integrate elements of both data sets, you can start to answer questions
like, “Who are my best customers?” “What is the next best offer?” Combining some key information from each set of data would allow you to create the best customer experience.

Data Federation

What is it? Data federation is a special kind of data integration. The ETL and ELT types of data integration combine data and then store it elsewhere for use, in the past within a data mart or data warehouse. But what if you simply want to look at the combined results without the need to move and store it beforehand? Data federation provides the capacity to do just that, allowing you to access the combined data at the time it is requested.

Why is it important? While many ETL and ELT data integration tools can run very fast, their results can only ever represent a snapshot of what happened at a certain point in time when the process ran. With data federation, a result is generated based on what the sources of data look like at the time the result is requested. This allows for a timelier and potentially more accurate view of information.

Imagine you’re buying a gift for your child at the store. As you check out, you receive an offer for another item that complements the gift you’ve chosen and happens to be something your child would enjoy. Even better – the item is in stock in the same store. Thanks to real-time analysis of next-best offer data and location data, the retailer
enhances your shopping experience by delivering a convenient, relevant offer to you at the right time and the right place.

Data Governance

What is it? Data governance is the exercise of decision-making authority over the processes that manage your organization’s data. Or to put it another way, it’s making sure that your data strategy is aligned to your business strategy.

Why is it important? Data governance starts by asking general business questions and developing policies around the answers:

How does your organization use its data?
What are the constraints you have to work within?
What is the regulatory environment?
Who has responsibility over the data?

Once the answers to these questions are known, rules that enforce them can be defined.

Examples of such rules might be defining what data users can access, defining which users can change the data versus simply view it, and defining how exceptions to rules are handled. Data governance tools can then be used to control and manage the rules, trace how they are handled, and deliver reports for audit purposes.

The auditability aspect of this is perhaps the most vital, as the organization’s leaders have to sign off on the accuracy of financial reports to governance boards, shareholders, customers and governmental bodies. It’s a heavy responsibility and one that carries the risk of censure, heavy fines and even legal action if not handled correctly.

Master Data Management

What is it? Master data management (MDM) is a set of processes and technologies that defines, unifies and manages all of the data that is common and essential to all areas of an organization. This master data is typically managed from a single location, often called a master data management hub. The hub acts as a common access point for publishing and sharing this critical data throughout the organization in a consistent manner.

Why is it important? Simple: It ensures that different users are not using different versions of the organization’s
common, essential data. Without MDM, a customer who buys insurance from an insurer might continue to receive marketing solicitations to buy insurance from the same insurer. This happens when the information managed by the customer relationship database and marketing database aren’t linked together, leading to two different records of the same person – and a confused and irritated customer.
With master data management, all organizational systems and data sources can be linked together and managed consistently on an ongoing basis to make sure that any master data used by the organization is always consistent and accurate.

In the big data world, MDM can also automate how to use certain data sources, what types of analytical models to apply, what context to apply them in and the best visualization techniques for your data

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IT APPLICATION AND SPECIFICATION

Requirement Gathering
Application Design
Website Design
Content Management Systems

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To support mobile devices, organizations should consider new technologies to boost the speed, security and management capabilities of their networks.

NETWORK SECURITY IS a huge challenge for organizations that have yet to determine how to efficiently manage mobile and cloud technologies.

Users won’t relent until they have access to business applications and content available on their own devices from any location. That requires IT to put preventive measures in place for corporate content that users access from any network. All organizations have vulnerabilities of one form or another, making security an economic imperative.

Attacks take many forms and can be difficult to detect. Sophisticated malware thwarts detection by using zero-day exploits and stealthy survival mechanisms. As a result, modern web, firewall and advanced threat protection (ATP) tools are compelling options for securing mobile networking.

These technologies redefine on-demand network functionality while enabling security controls that don’t degrade network performance. At the same time, IT still needs to use traditional network security methods—such as URL filtering, antivirus and antimalware detection and web application controls—to fortify the perimeter.

Granular app-level security policies aren’t new, but next-generation firewall and ATP can add contextual elements. For instance, admins can take advantage of user and device location data to enforce fine-grained security policies through enterprise mobility management. This integration can also help IT enhance Wi-Fi security by allowing admins to set policies around users’ connectivity to the company wireless network.

Another big advancement in networking that mobile admins should take advantage of is software-defined networking (SDN), which essentially decouples traditional networking hardware and software. Software-defined networks measure throughput in real time, bringing on-demand bandwidth to support the expanding data usage patterns of the cloud
and mobile age. Network function virtualization (NFV) technology can complement SDN by boosting operational speeds, enabling fast provisioning and improving the scalability of virtual firewalls, load balancers and intrusion detection systems.

SDN and NFV technologies can help automate security and policy enforcement through responsive protection across the entire attack continuum. Organizations should evaluate these technologies as part of any modernization initiative. These technologies are new, however, and vendors are still developing the global multi-tenant infrastructures needed to effectively deliver them with the quality of services and scalability businesses
will require.

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An IT General Controls audit examines how well IT systems and applications are performing. If an audit indicates that certain controls are not being done correctly, those issues are considered risks to the IT department and its ability to function.

Nearly every one of the 18 items in six controls listed below is designed to prevent situations that threaten data center operations. Results of the ITGC audit also provide an effective assessment of the risk level to the infrastructure. They identify areas where improvement is needed, which can help reduce risk.

Let’s go over the details of six controls that are often part of an ITGC audit:

Control 1: Physical and environmental security

• Server room is locked with a card access system.
• A limited number of employees have card access to the server room.
• The data center has raised floors and water detectors under the floors.
• A heating, ventilation and air conditioning (HVAC) system alarm sends emails and launches audible signals if there is a system failure.
• Server room fire extinguishers are checked quarterly.

Control 2: Logical security

• New employees are provided access to system resources after being approved by HR.
• Terminated employees have their access credentials deleted within 15 minutes of notification by HR.
• Windows Active Directory is used to authenticate users requesting system resources.

Control 3: Change management

• Test and production environments are segregated from each other.
• Production changes and patches are tested, documented and approved before being placed into service.

Control 4: Backup and recovery

• Data is backed up daily according to a documented process and schedule.
• Disaster recovery plans are in place for critical systems and are tested annually.

Control 5: Incident management

• Daily activity reports are generated for review by IT management.
• An incident response process is documented and used regularly when responding to abnormal situations.

Control 6: Information security

• Firewalls are used to protect the network perimeter from suspicious activities.
• Antivirus software is used to prevent damage from viruses.
• Incoming and outgoing data traffic is monitored 24/7 to identify potential phishing attacks, distributed denial-of-service attacks and other attempts to penetrate the network perimeter.
• Penetration testing is performed twice annually to check for vulnerabilities.

Performing the ITGC audit

When performing the ITGC audit, examine each of the controls using a combination of techniques:

1. Interviews with employees (and their managers) responsible for them.
2. Examination of documentation (such as written procedures, policies and technical manuals).
3. Personal observations (for example, watching how an individual performs tasks relating to the control).

When writing up the findings from your interviews and examinations, you’ll be in a better position to rationalize if a control is being performed properly.

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The 11 Security Commandments

The Jericho Forum commandments define both the areas and the principles that must be observed when planning for a de-perimeterized future.
Whilst building on “good security”, the commandments specifically address those areas of security that are necessary to deliver a de-perimeterized vision.
The commandments serve as a benchmark by which concepts, solutions, standards, and systems can be assessed and measured.

Fundamentals

1. The scope and level of protection should be specific and appropriate to the asset at risk.
• Business demands that security enables business agility and is cost-effective.
• Whereas boundary firewalls may continue to provide basic network protection, individual systems and data will need to be capable of protecting themselves.
• In general, it’s easier to protect an asset the closer protection is provided.
2. Security mechanisms must be pervasive, simple, scalable, and easy to manage.
• Unnecessary complexity is a threat to good security.
• Coherent security principles are required which span all tiers of the architecture.
• Security mechanisms must scale; from small objects to large objects.
• To be both simple and scalable, inter-operable security “building blocks” need to be capable of being combined to provide the required security mechanisms.
3. Assume context at your peril.
• Security solutions designed for one environment may not be transferable to work in another. Thus, it is important to understand the limitations of any security solution.
• Problems, limitations, and issues can come from a variety of sources, including geographic, legal, technical, acceptability of risk, etc.

Surviving in a Hostile World

4. Devices and applications must communicate using open, secure protocols.
• Security through obscurity is a flawed assumption – secure protocols demand open peer review to provide robust assessment and thus wide acceptance and use. •The security requirements of confidentiality, integrity, and availability (reliability) should be assessed and built in to protocols as appropriate; not added on. • Encrypted encapsulation should only be used when appropriate and does not solve everything.
5. All devices must be capable of maintaining their security policy on an un-trusted network.

• A “security policy” defined the rules with regard to the protection of assets
• Rules must be complete with respect to an arbitrary context.
• Any implementation must be capable of surviving on the raw Internet e.g. will not break on any input.

The Need for Trust

6. All people, processes, and technology must have declared and transparent levels of trust for any transaction to take place.
• Trust in this context is establishing understanding between contracting parties to conduct a transaction, and the obligations this assigns on each party involved.
• Trust models must encompass people/organizations and devices/infrastructure.
• Trust level may vary by location, transaction type, user role, and transactional risk.
7. Mutual trust assurance levels must be determinable.
• Devices and users must be capable of appropriate levels of (mutual) authentication for accessing systems and data.
• Authentication and authorization frameworks must support the trust model.

Identity, Management, and Federation

8. Authentication, authorization, and accountability must interoperate/exchange outside of your locus/area of control.
• People/systems must be able to manage permissions of resources and rights of users they don’t control.
• There must be capability of trusting an organization, which can authenticate individuals or groups, thus eliminating the need to create separate identities.
• In principle, only one instance of person/system/identity may exist, but privacy necessitates the support for multiple instances, or one instance with multiple facets.
• Systems must be able to pass on security credentials/assertions.
• Multiple loci (areas) of control must be supported.
Access to Data
9. Access to data should be controlled by security attributes of the data itself.
• Attributes can be held within the data (DRM/metadata) or could be a separate system.
• Access/security could be implemented by encryption.
• Some data may have “public, non-confidential” attributes.
• Access and access rights have a temporal component.
10. Data privacy (and security of any asset of sufficiently high value) requires a segregation of duties/privileges.
• Permissions, keys, privileges, etc. must ultimately fall under independent control, or there will always be a weakest link at the top of the chain of trust.
• Administrator access must also be subject to these controls.
11. By default, data must be appropriately secured when stored, in transit, and in use.
• Removing the default must be a conscious act.
• High security should not be enforced for everything; “appropriate” implies varying levels with potentially some data not secured at all.
Conclusion
De-perimeterization has happened, is happening, and is inevitable; central protection is decreasing in effectiveness:
• It will happen in your corporate lifetime.
• Therefore, you need to plan for it and should have a roadmap of how to get there.

Research Source : Jericho Forum

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