UVOD

PROF. DRAENA GAPAR 02.12.2013.UPRAVLJANJE POSLOVNIM PODACIMA (BUSINESS DATA MANAGEMENT)

Sadraj Uvod i definiranje pojmovaIzvori podatakaSkladite podatakaDimenzijsko modeliranjeUpravljanje podacima (Data management)Upravljanjem ivotnim ciklusom informacija (Information Lifecycle Management)Kvaliteta podataka

LiteraturaInmon, W.H.: Building the Data Warehouse 4th Edition, Wiley Inc. USA, 2005. Inmon, W.H., Strauss, D., Neushloss,G.: DW2.0 The Architecture for the Next Generation of Data WarehousingKimball,R., Ross,M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, Wiley Kimball,R., Ross,M., Thornthwaite,W., Mundy,J., Becker,B.: The Data Warehouse Lifecycle Toolkit, Wiley Inc. USA, 2008

LiteraturaPetrocelli, T. : Data Protection and Information Lifecycle Management, 2005

Reid,R.S., Frase-King,G., Schwaderer,W.D.: Data Lifecycles: Managing Data for Strategic Advantage, 2007 Loshin, D.: The Practitioners Guide to Data Quality Improvement, 2011

OCJENJIVANJE

Priprema za svako predavanje na zadanu temu +DVA naina

Implementacija od problema do skladita podataka i izvjea

Analiza upravljanja podacima u konkretnom poduzeu

Analiza upravljanja podacima u konkretnom poduzeuPronai poduzee za case studyAnaliza izvora podataka za donoenje odluka u odabranom poduzeu prezentiranje rezultataIstraiti mogunosti uporabe drugih izvora podataka prezentiranje rezultataIzrada dimenzijskog modela za data mart prezentiranje rezultataDefiniranje izvora podataka za data mart prezentiranje rezultataIzrada Politike upravljanja podacima prezentiranje rezultataPreporuke za unapreenje upravljanja podacima u poduzeu

ZATO

Upravljanje poslovnim podacima

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WHY Business Data ManagementBusinesses and other organisations need information for many purposes:

Planning. To plan properly, a business needs to know what resources it has (e.g. cash, people, machinery and equipment, property, customers). It also needs information about the markets in which it operates and the actions of competitors. At the planning stage, information is important as a key ingredient in decision-making.Recording. Information about each transaction or event is needed. Much of this is required to be collected by law - e.g. details of financial transactions. Just as importantly, information needs to be recorded so that the business can be properly managed.Controlling. Once a business has produced its plan it needs to monitor progress against the plan - and control resources to do so. So information is needed to help identify whether things are going better or worse than expected, and to spot ways in which corrective action can be takenMeasuring. Performance must be measured for a business to be successful. Information is used as the main way of measuring performance. For example, this can be done by collecting and analysing information on sales, costs and profits

WHY Business Data ManagementDecision-makingInformation used for decision-making is often categorised into three types:Strategic information: used to help plan the objectives of the business as a whole and to measure how well those objectives are being achieved. Examples of stategic information include:- Profitability of each part of the business - Size, growth and competitive structure of the markets in which a business operates - Investments made by the business and the returns (e.g. profits, cash inflows) from those investmentsTactical Information: this is used to decide how the resources of the business should be employed. Examples include:- Information about business productivity (e.g. units produced per employee; staff turnover) - Profit and cash flow forecasts in the short term - Pricing information from the marketOperational Information: this information is used to make sure that specific operational tasks are carried out as planned/intended (i.e. things are done properly). For example, a production manager will want information about the extent and results of quality control checks that are being carried out in the manufacturing process.

GDJE SE NALAZE

Poslovni podaci ??

WHY Business Data Management

Rules for collecting and utilizing business data effectively

Know what you're looking for

Garbage in, garbage out

Trust the data

KNOW WHAT YOU'RE LOOKING FOR

It's difficult to collect and analyze all the data your business produces unless you know what you're looking for. You need to identify certain metrics that, given the nature of your business, will provide insight into how you can run the shop better (aside from the obvious, such as revenues and profit figures). Big businesses call these "key performance indicators," or KPIs. I call them common sense. For example, if you publish a magazine, you need to know how well each of your sales associates is performing, the renewal rate for advertisers in various industries, and the demographic information of your readers.

GARBAGE IN, GARBAGE OUT

Not all facts are created equal. That's to say that though you may be collecting and storing information, if it's not accurate, relevant or current, it's likely to hurt more than help. Data management specialists call this "garbage in, garbage out." If your data is not clean, it will yield dirty and misleading analyses. So if you're going to use data to support business decision making, be sure it's scrubbed.

TRUST THE DATA

Once you've collected the right information, and you're certain it's accurate, put it to use. I've spoken with many business owners who collect data and view the analyses, but don't act on it. The data is there to tell you something. It's the most objective source of advice you're going to get. It holds no bias. It's the story of your business laid bare. And if it's telling you to change something, change it.

*/27Does organisation need data management?There are arguing about data ownershipThere is prevailing opinion that organisation first has to formalise processesof decision makingThere is no sense to fix data or one system because many of them have more ownerships in company There is agreement about bad data quality, but there is no agreement about ways for resolving that problemCRM, SCM and ERP are in the dead lock statusThere are more versions of the same truth.

*/27Withouth data managementConfusionData fragmentationEmployees frustration More human workMore arguingHigher costsNo posibility to establish data flows between different systemsProblems during interation systems There is no trustworthy performance indicators

DEFINITICIJE

Data management is the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner.http://searchdatamanagement.techtarget.com/definition/data-management

Business dataInformation about people, places, things, business rules, and events, which is used to operate the business. It is not metadata. (Metadata defines and describes business data.)

http://www.information-management.com/glossary/b.html

Information managementInformation management, in a broad sense, means organization of information. There are three stages in the handling of data in this situation:1.Gathering information from various sources. 2. Comprises of organizing the information as per the requirements of the user. 3. The final stage consists of dispatching information to whoever requires it. Organization of both physical and electronic information like paper text, video and audio files and electronic documents, is included under the concept of information management. The major objective of this concept is to help companies or organizations provide better services to their customers.

data governanceData governance is the practice of organizing and implementing policies, procedures and standards for the effective use of an organization's structured/unstructured information assets.

http://www.information-management.com/glossary/d.html

data managementControlling, protecting, and facilitating access to data in order to provide information consumers with timely access to the data they need. The functions provided by a database management system.

Data management is an overarching term that refers to all aspects of creating, housing, delivering, maintaining and retiring data with the goal of valuing data as a corporate asset.

http://www.information-management.com/glossary/d.html

data stewardThe data steward acts as the conduit between information technology (IT) and the business portion of a company with both decision support and operational help. The data steward has the challenge of guaranteeing that the corporation's data is used to its fullest capacity.

Enterprise information management is the superset that refers to the people, processes and technology dedicated to gathering, managing, disseminating, leveraging and disposing of all information assets used by an organization.

Master data management (MDM) is synchronized enterprise-wide business data that provides definitions and identifiers of internal and external objects involved in business transactions (e.g., customer, product, reporting unit, market share).Master Data is the critical business information supporting the transactional and analytical operations of the enterprise. Master Data Management (MDM) is a combination of applications and technologies that consolidates, cleans, and augments this corporate master data, and synchronizes it with all applications, business processes, and analytical tools. This results in significant improvements in operational efficiency, reporting, and fact based decision-making.

Fragmented inconsistent Product data slows time-to-market, creates supply chain inefficiencies, results in weaker than expected market penetration, and drives up the cost of compliance. Fragmented inconsistent Customer data hides revenue recognition, introduces risk, creates sales inefficiencies, and results in misguided marketing campaigns and lost customer loyalty1. Fragmented and inconsistent Supplier data reduces supply chain efficiency, negatively impacts spend control initiatives, and increases the risk of supplier exceptions. Product, Customer, and Supplier are only three of a large number of key business entities we refer to as Master Data.

Data Lifecycle managemene (DLM) is a policy-based approach to managing the flow of an information system's data throughout its life cycle: from creation and initial storage to the time when it becomes obsolete and is deleted. DLM products automate the processes involved, typically organizing data into separate tiers according to specified policies, and automating data migration from one tier to another based on those criteria. As a rule, newer data, and data that must be accessed more frequently, is stored on faster, but more expensive storage media, while less critical data is stored on cheaper, but slower media (Hough, 2008).

DLM architectures would typically include an archiving system that indexes all critical and compliance-related information, backs it up and stores it where it can't be tampered with and can be discovered and accessed in a reliable and timely fashion. Deduplication and compression of all files ensure efficient usage of available storage space.

Information Lifecycle managemene (ILM) is a comprehensive approach to managing the flow of an information system's data and associated metadata from creation and initial storage to the time when it becomes obsolete and is deleted. Unlike earlier approaches to data storage management, ILM involves all aspects of dealing with data, starting with user practices, rather than just automating storage procedures and in contrast to older systems (for example hierarchical storage management HSM), ILM enables more complex criteria for storage management than data age and frequency of access. (SearchStorage, 2004).

It is important to stress that ILM is not just technology. ILM integrates business processes and IT in order to determine how data flows through an organization, enabling users and managers to manage data from the moment it is created to the time it is no longer needed

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