Data Integrity - Qualified Data!
Data quality is vital when determining whether the operations of companies are successful or not. Many big companies are investing millions of liras into management information system in order to improve their operations and make them more efficient. But no matter how excellent and expensive these systems are, the accuracy of their output completely depends on data it contains.
Due to inadequacy of data quality, the desired results can’t be obtained from many systems which are created by spending a lot of time and money. The information that is obtained by wrong, inconsistent and contradictory data leads companies to wrongly-made decisions, risks, increasing costs, lost customers and lost work. Clean, qualified and integral data helps decision-makers to make more correct decisions about their job and saves from potential risks.
As Excel Çözümleri, we provide services about data cleansing, data merging, data enrichment and data mining, and we help you in making correct decisions by providing correct reports and analysis.
Data Cleansing (Data Standardization, Data Parsing)
The term data cleansing generally means scanning for wrong and faulty data in the database, parsing and correcting them. With our data cleansing service, we convert and merge the data in different formats, remove the wrong and excess data, standardize the data which will be commonly used, parse them in order to make them more useful; therefore we make sure that consistent and reliable information is provided by the database.
We take a data that is held in a single cell under full name and parse it into columns as Title, Name, second name, Surname which can be recognized by the computer. For the parsing of complex projects which contain thousands of data recorded before, we determine many different rules from time to time and obtain an acceptable success rate.
“Istanbul” which is written in different forms, is standardized and made a single name.
Data merging is the merging of data in many different sources into a single database. During merging, unnecessary and recurring data is eliminated, data is ensured to be single and together. If required, data cleansing can be used during data merging.
Data merging is a necessary and commonly used method among establishments. Evaluating the data which are held in different sources together, turning them into comparable data, and using them during strategic decision is required. Thanks to data merging, you can prevent having the same data more than one, look at the data more integrally, and enjoy a faster access. Data storage and process costs can be reduced.
The values that are recorded in two tables and contain recurring data, has been merged into one table in order to be more useful and allow faster access to information.
It can be called data enrichment or data development. In accordance with the data that is obtained from internal or external data source, new data which can be obtained through analytical evaluation is added to current data line.
Data Mining, Forecasting and Reporting
Data mining is the searching of relations that may help us make estimations about the future in big data piles through the use of computer programs. Thanks to data mining, we can reveal a hidden model and rules inside the data. This process can be done with pivot tables, forecasting method or statistical studies. Data mining plays a very important role during decision-making. Thanks to data mining, we can forecast the business opportunities beforehand, establish correct models about our job and determine the potential risks beforehand.