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Data Lifecycle Management – Handling data’s full lifecycle inside an information system.

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data lifecycle management

 

Technologies and markets, which are currently dealing with data storage and management,  are finding themselves in a dramatic change, because of the continuously growing amount of data and information gathered. The policy-based approach which manages the path of data in a certain Information system throughout its lifecycle, from creation, storage and deletion after the data is outdated, is called “Data Lifecycle Management.” Internet sales, digital contracts, CRM data, operational systems, marketing all of which are nowadays the base, on which every modern enterprise function. Not to mention wireless management, remote devices, and multimedia usage, all of these producing huge amounts of data. Heavy amount of data to store, monitor, audit and destroy, not only to do business with but also to comply with data legislation.

 

Data Lifecycle Management goals.

 

 

  • Data Security

 

 

Security of the stored data has become the main topic not only towards IT management. Due to Data-Protection policies and other law enforcement, security issues nowadays involve board management. So besides treating  IT departments as business departments, it is also important to secure IT infrastructure. Data lifecycle management role is to ensure the cost-effective protection of data. Which means that important data are given a high level of protection, and otherwise unimportant data are given an adequate and cost-effective level of protection.    

 

 

  • Availability of Data

 

 

Gathered data has to be stored and accessed at different frequencies depending on their importance. Availability of data is regulated with DLM (Data Lifecycle Management) by storing important data in primary storage devices (CPU Cache, RAM) with short access time. And data, which is less important, is stored in secondary and tertiary storage devices like Hard Drive or even in offline devices (USB, CD, Tape Drive) always in accordance to the data importance and frequency of usage. To regulate this DLM uses technology based on HSM (Hierarchical System Management).

 

 

  • Data Integrity

 

 

Data integrity has become a serious issue due to regulations regarding data usage. The integrity of data means that data, which is stored in enterprise databases, must be accessible, reliable, and authentic over its lifecycle. According to FDA and their guide on “ Data integrity and compliance with CGMP” data is expected to be :

 

  1. Attributable – Collected data has to be attributable to the person who generated that data.
  2. Legible – Data gathered has to readable, and easy to understand for humans. Ensured records have to be accessible during the data lifecycle.
  3. Contemporaneous – Every time, data is executed, records have to be made. Results and measurements have to be recorded at the time data are executed.
  4. Original – Original form or source data have always to be preserved.
  5. Accurate – Data has to be free from errors, truthful, and reflective of the perceptive.

 

Data lifecycle management has to give solutions in order to keep data hierarchically tiered in-store technologies, that differ in price and performance. While all these data have to be accessible to users and admins every time.

 

HSM ( Hierarchical Storage Management)

 

Hierarchical Storage Management is a Data Lifecycle Management concept. HSM operates like a  backup software, a low frequency used files and data are stored in a cheaper backup storage. Data is sorted and administered according to specific parameters such as last access, the frequency of use, file size etc. The storage media used to store the data also relies on these parameters. So often data is stored in hard disks, and later when the frequency of usage is higher, data is swapped out to tape drives, or tape libraries. Inactive files and data are stored and entered in the local file system and can be recalled anytime. The more active data is stored in more expensive mass storage systems and can, therefore, be recalled anytime but faster.

HSM system as part of the Data Lifecycle Management process offers a longer access time to stored data, that’s why they are more practical for DLM.

 

One way to keep costs under control while still providing for the unexpected need for specific data is to visualize and act upon the five phases of the data lifecycle:

 

Phase 1: Fresh data is a “ Source of Power” for companies. Employees, create new data and files, which flow into the company’s daily operations. Companies store this data into local and network servers. In addition, these data is also backed up in the cloud or locally. So in case of data loss, they can be restored quickly.

 

Phase 2: Data loss can be prevented through data protection. When data is aged they can be removed from primary servers, and transferred into more cost-effective locations like external backup tapes, or cloud servers. In case of a major disruption, or a catastrophic event the stored data can be completely restored.

 

A balanced data backup and recovery strategy combine external storage tape libraries with cloud backup and data recovery. In such a hybrid system, the most suitable storage medium is selected for each file.

 

Phase 3: Another way to keep the data storage costs within limits is archiving. For some data, there is a retention period of up to ten years. So even inactive data must be stored and be accessible during this period. For long-term data storage requirements, external backup archives provide a high level of security with fast access and low storage costs.

 

This kind of storage is especially well suited for unstructured data such as emails. Companies also prefer these archives because the large amounts of data needed for big data analysis can be stored there cost-effectively.

 

Phase 4: Old and inactive data has to be destroyed, so controlled data destruction has to be ensured. Backup tapes are cheap but most companies can not afford unlimited storage. The last phase of Data Lifecycle Management is a reliable destruction of no longer needed data. This is usually regulated and recorded according to a fixed timetable, which is based on the regulatory and legal requirements as well as the requirements of the company.

 

Phase 5: Finally, a secure deletion must take place from all IT systems. The data storage lifecycle ends only when the last traces of data are erased – even from decommissioned computers and peripheral devices. As with the destruction of data media, the gapless chain of evidence must be maintained when old computers and office equipment are removed.

 

If your organization only retains out-of-date storage hardware and storage to access legacy data when needed, it may be a good idea to hire a contractor to handle the data migration and ensure data recoverability. With special technology, such a service provider provides access to archived data and saves your company the expense associated with maintaining legacy systems and legacy software.

Business Process Mining – Leverage additional value by automating routine tasks.

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Business Process Mining                                                                                                Business process mining is a new innovative way of improving business processes. Capturing, analyzing and automating business processes is a new discipline in Information Technologies. The so-called internet of processes enables enterprises to automate specific business processes, by using data science. The processes which a back office worker makes during his work routine, are modeled and analyzed. As a process management technique, it helps businesses understand and optimize business processes in a faster and better aspect.  The process participants can concentrate on why the process flows are the way they are, after gathering data.

Business process mining goals.

The goal of process mining is not to analyze data, but to improve processes. There are two perspectives when it comes to improving a process, the performance perspective, and the compliance perspective. So main answers that a process mining improvement should answer are always regarding these two perspectives. To answer these questions a process analysis with all the process data has to be made. So process mining is situated between the analysis technique like simulation and process modeling without considering real data like data mining. Process mining is about analyzing data including event logs, the amount of data gathered and analyzed is used to visualize the process flows.

Process mining has to fill a gap between these two forms of analyzing a situation and give solutions, which improve a process. Business process mining and improvement include three stages:  process discovery, conformance checking, and enhancement of the business process.

  • Business process discovery

Process discovery is the first and most typical technique of process mining and improvement.

In this stage, a process model is derived, which represents the actual process execution using login data. Process discovery is a stage, that is used to get insights into how the process flow goes and how the process is carried out.

  • Process mining and conformance checking  

Conformance checking is the second step in the process mining and improving the procedure. In the conformance checking stage, the differences between a pre-defined process model and the data records in the event log are compared. This comparison enables to find cases and gaps, which are executed differently as a process respecting standards, project policies and guidelines. So the conformance checking measures the hardness of the difference between the ideal process and the current process.

  • Business process enhancement

The last stage in business process mining is the enhancement of the process. During the enhancement stage, the process model will be enlarged. Data gathered from process mining and other conventional processes make it possible to observe the business process in different viewpoints. During enhancement, it is possible to use the time information relating to business operations in the event log, and this opens the opportunity to analyze the process performance from the time perspective. It is also possible to analyze the previous cases together with the execution paths. This enables the analyzing of the effects on elements in a decision-making point. This kind of analyze makes it possible to utilize traditional data mining techniques such as decision-making tree.

Elba Technologies is your trusted advisor in Business Process Automation

And after identifying the process flows, the steps and stages of a certain process can be automated and repeated without additional costs. This kind of process automation has used hundreds of companies around the globe. And experienced effects such as cost reduction and unleashed value through process mining.

Robotic Process Automation – Improving operations while reducing costs.

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Robotic process automation            Robotic process automation or RPA is more than a trend, it is a different way to think how processes solutions are delivered and managed. In today’s’ digital world, helping people navigate towards financial confidence, requires a fundamental shift in how you apply key operational navigations.

Elba Technologies knows how to help clients to continually adapt to a changing digital environment, and address the challenges that this presents. Work areas are burden with repetitive and time-consuming processes.

Clients want to reduce costs, improve delivery and ensure consistent delivery. Robotic process automation or RPA is more than a trend, it is a different way to think how processes are solution delivered and managed. Robotic process automation further advises knowledge workers and judgment based role staff.

By removing Monday and allowing them to spend their time on the parks, in business processes,  which are customer-centric and not rule driven. Humans and machines won’t be enough to drive business in the coming decades; the enterprises of tomorrow will be the one who knows to melt the two effectively.

Taking steps towards a virtual workforce to that end automation delivers proven results. When it comes to quality, the ability to create a full audit trail reduces compliance and business risk because the human error is eliminated.

While there is a greater than 40% increase in FTE’s opportunity to focus on customers.

In the area of delivery, RPA can lead to a 40% reduction in average handling cycle time, enhance the customer experience and make a business resilient and operational 24/7. And when it comes to cost, businesses can see processing costs reduced by 30-80 % driving ROI on quarters vs. years.

Elba Technologies has already used RPA to automate a number of processes across different industries.

One of the many processes that are automated with RPA is i.e the residential mortgage loan initiation. This process is responsible for creating a new loan in a Loan Origination System, ordering services and validating key information these people-intensive process, for example, faces numerous challenges including heavy data input and system runtime, multiple third-party orders to external systems using existing data resulting in double entry.

In RPA developers use business process documentation, to map out and build the solution.

Process flows are created the tool is thought about the target application which, needs to interact with and the end to end automated business process is successfully modeled in the system. As it easily navigates through target applications, it executes the business process just as a human would.

The RPA tool initiates the new loan set-up and creates the loan, and then it inserts data within the fields correctly every time.

It gathers data and initiates third-party orders and with a virtual robot, it can for example access external websites for validation. In this process, for example, a quality check on addresses.

In comparing manual entry to RPA, the time saved is significant an approx. 20 min for each loan set-up and ordering service. Plus with the virtual worker, there is the benefit of time accuracy and predictive time orders.

With the RPA tool automation, the virtual worker moves directly from one item to the next, until all items in his queue are exhausted.

Time saved quickly adds up during an 8 hour period by saving an approx. 20 min in processing for each file and an additional 140 min will be freed up for other work.  This excludes breaks, errors and out of office time.

And if a virtual worker operates for 16 hours a day, the savings double to 280 min, that’s equal to almost 5 hours of added capacity a day per person. With this process, we can see an up to 50% reduction in FTEs required.

Robotic process automation will drive operational efficiencies and cost savings across core lines of business. Transforming customer outcomes, reducing business risk and optimizing existing processes and systems.

Elba Technologies can help you achieve new levels of operational excellence.

Robotics & Cognitive Automation – When RPA meets artificial intelligence.

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Robotics & Cognitive Automation – When RPA meets artificial intelligence

Robotic Process Automatation

Robotic Process Automation is a way for rapid automation of knowledge work with up to six times faster break even than traditional BPM Suites or Enterprise Application Integration. The McKinsey Global Institute announces the automation of knowledge work as one of the twelve technologies, that have the potential to sustainably disrupt our future and lines it up with technologies like autonomous vehicles, cloud and renewable energy*. It fits into any enterprise architecture without the need for process, or technology re-engineering by providing inexhaustible virtual workers that become integrated into your organization as value-adding resources. However, the possibilities of RPA are bound to structure digital data with foreseeable business rules. This is, where cognitive automation comes into play, lending its capabilities to mimic human intelligence with data mining, pattern matching, natural language processing, machine learning, machine reasoning, and other skills. By extending RPA with cognitive capabilities, robots become dynamic and flexible virtual workers that execute tasks provided by cognitive components, releasing the real value of knowledge work automation.

 

What business challenges to overcome with Robotics& Cognitive Automation?

  • Repetitive tasks with high transaction volumes binding employee potential

Despite growing digitization and digitalization, there are still many processes to be found in enterprises, where valuable employee potential is wasted to manually work off thousands of transactions, collecting data, applying business rules and entering data on applications. Robotics & Cognitive Automation release this employee potential by taking over repetitive, rule-based work and mimicking human decisions in data-driven environments.

  • High processing time and poor quality of results

Especially in process environments with high time pressure and frequent repetitions, slips of the pen and other errors are a deeply human characteristic, that negatively affects the quality of process outcomes and leads to time-consuming rework. Robots work multiple times faster than humans and at the same time exactly follow their built-in business rules, automatically reacting to process exceptions and failure states. This leads to a massive increase in productivity with a high and stable quality of results.

  • High processing cost

Recruitment and onboarding, training and education, payroll, shift allowance, HR administration, benefits programs and more – all those factors add up to an employee’s total cost of ownership and therefore are part of the cost for manual processing. High employee turnovers, especially in shared services or offshore centers, make these costs even more critical. Compared to that, robots provide you with stable and scalable capacity 24x7x365 without vacation, sick leave or any other diversion. All this for roughly a ninth of the cost for onshore labor time or a third of the cost for offshore labor time. Once the robots are implemented, a single operator manages an average of five robots, adding up to the capacity of 25 full-time equivalents. With growing capabilities of cognitive technologies and robots operating robots, this ratio will grow much further.

  • High risk and lack of compliance

A robot does not have any interest in compliance or any other harmful behavior, exactly stays to the rules that it is provided with and is equipped with state of the art IT security measures. Every single step performed by a robot can be tracked and traced for audit purposes. This does not only increase compliance with regulatory and other policies but reduces operative risk to a minimum.

What are the challenges of implementing Robotics & Cognitive Automation?

  • Catching on the disruptive market

As the market for Robotics & Cognitive Automation is heavily growing, there is a large number of software providers with different specializations and product strategies. While some of the vendors are pure RPA or cognitive automation players, others are building on their experience in one of the markets and strive to differentiate their business. Choosing the right selection of tools that match one’s business and automation strategy as well as process and IT landscape is therefore not an easy undertaking. It requires deep knowledge of different products, frequent market assessments as well as communications with the market leading providers to make a well-founded choice.

  • Identifying automation candidates

Once an automation tool has been selected, its technical implementation is the easy part, while ones’ journey to Robotics & Cognitive Automation has just begun. Without a clear approach for process identification, assessment, and prioritization, companies will find themselves incapable of scaling up their automation due to missing automation candidates. The reason for such a missing approach can be manifold but the result is to idealize robot capacity, but negating the business case for automation.

  • Establishing a governance structure

The success of Robotics & Cognitive Automation depends on a dedicated sponsor and the engagement of business as well as IT. Those stakeholders can be brought together in a Center of Excellence, establishing in-house automation capabilities that provide sponsorship, process expertise as well as IT and operational support. While there are different models for a Center of Excellence, Robotics & Cognitive Automation should be considered an operational asset that is driven by business. However, due to its impact on IT infrastructure, security, business continuity, and disaster recovery, it must comply with IT policies and requires IT infrastructure support in order to scale.

  • Overcoming people’s resistance

While Robotics & Cognitive Automation can bring huge benefits to an enterprise, it does not only change process delivery but is a significant digital transformation that changes the organization as a whole and the way people work. Such a cultural change faces resistance if people are left behind the automation. Dedicated change and communications management are therefore keys to a successful RPA implementation.

  • Shaping an automation strategy

In the face of the above-mentioned challenges, ad-hoc implementations of Robotics & Cognitive Automation are doomed to fail. Enterprises require a collective and communicated automation strategy, that is aligned with the overall business strategy in order to make the most of their automation and to realize its strategic benefits.

Why choose Elba Technologies as your trusted advisor?

  • Vast experience in process consulting and business automation

The Elba Technologies team has a combined experience of XX years in process consulting and business automation, that reaches far beyond the rise of robotics for knowledge automation. Combining our expertise of other means for process automation with the uprising capabilities of Robotics & Cognitive Automation, we support your enterprise in making well-founded decisions and help you realize the optimum of your business automation.

  • Specialized on Robotics & Cognitive Automation in the German (speaking?) market

As a specialized provider for Robotics & Cognitive Automation, Elba Technologies has a clear focus on bringing cognitive robotics into the German market by enabling our clients to grow into self-sustained automation experts. Our German-speaking advisors leverage their market experience to tailor automation solutions to your business demands and guide you through the establishment of your automation center of excellence.

  • Advanced and flexible contractual models

With our consumption based robotics as a service offering as well as value-share contracting options, Elba Technologies allows you to shift your automation cost from CapEx to OpEx and brings Robotics & Cognitive Automation into your enterprise with minimal pre-investments. This ensures a fast and positive return on investment that can go far beyond traditional automation solutions.

  • Extension of RPA capabilities with cognitive solutions

Elba Technologies does not rely on the capabilities of pure RPA but combines best in class RPA tools with state of the art cognitive solutions, making the robots more flexible and widening the choice of use cases beyond fully structured digital data.

  • Technology independence and partnership with major market players

Elba Technologies is concentrated on optimizing the business value of your Robotics & Cognitive Automation and chooses the best combination of software to support this target. In order to be ahead of the market, we collaborate with the best in the class of RPA, cognitive solution providers and frequently assess the market to extend our business relationships.

  • Unique Robotics & Cognitive Automation intellectual property

Elba Technologies develops state of the art apps to support the assessment of your process landscape, the identification of automation candidates as well as the management and operations of your robots. These apps are built on the unique intellectual property and lead away from excel driven advisory and operations to an enterprise approach