| Have you been underestimating data quality issue? | | | | • Mismanaged data |
| Data quality is often addressed in the form of | | | | • Inadequate data acquisition process |
| employees or vendor-supplied consultants, | | | | • Multiple data silos maintaining the same data |
| accounting for 20-50% of the data warehouse | | | | • Redundant data across multiple channels |
| projects, labor over at least a few weeks and, in | | | | • Ineffective data update process |
| some cases, several months, depending on | | | | It is important to identify the weak sources within |
| project size and complexity. Data, most often, | | | | an enterprise from where erroneous data flows. |
| loses quality when integrated. Traditional | | | | Over the years, enterprises tend to maintain data |
| integration techniques most often fail as data | | | | silos that keep expanding with faulty records. Due |
| flows from multiple sources (built on different | | | | to disjointed network and insufficient integration |
| platforms) and in different formats. All data | | | | capability, the data is further contaminated with |
| sources might not have effective information | | | | erroneous information. Inaccurate, redundant and |
| sharing mechanisms, which primarily makes | | | | missing data continues to grow till it becomes an |
| integrated data unreliable. | | | | unmanageable set of fragmented and voluminous |
| Poor data quality is known to damage million | | | | databases. Different systems are programmed to |
| dollars for any enterprise. Spending on | | | | access data from the different faulty databases. |
| implementing large CRM, BI or integration projects | | | | CRM, ERP, BI and integration technologies that fail |
| are a waste till the quality of data flowing to | | | | in enterprises are most often the result of |
| these systems remains low. In fact in the long | | | | substandard data. The data quality metrics are |
| run, bad data can lead to ‘low customer | | | | only the roadmap to improve the quality of data. |
| satisfaction’ and decreased customer | | | | Establishing a process to monitor results and set |
| retention. The three aspects critical to data are | | | | improvement goals is the next vital phase of data |
| accuracy, consistency and timeliness. High quality | | | | quality lifecycle. Cost, effort and time spent on |
| data is dependent on these three standards. | | | | the data quality process should be able to provide |
| Inaccurate data is junk for any enterprise, thus, | | | | business benefits, accordingly. The improvement |
| accuracy is important. Though all departments of | | | | process is a set of business cases and specific |
| an enterprise need data for different purposes, it | | | | areas that need to be addressed. With the |
| is crucial for the entire enterprise to have | | | | improvement areas, the goals and the priority |
| consistent data. In 86% cases, low customer | | | | attached with each should be clearly identified. |
| satisfaction is the result of obsolete customer | | | | Personnel responsible for executing each |
| data that may exist in several departments as | | | | improvement task should clearly understand the |
| redundant customer records. Duplicate customer | | | | entire execution process. The improvement |
| records also increase the volume of databases. | | | | process can be taken as the yardstick to |
| Data at the right time, for the right people | | | | measure future data quality improvement. |
| determines the operational efficiency of any | | | | According to Gartner, through 2005, IT-driven |
| enterprise. It is the right data that becomes the | | | | data quality efforts will be largely ineffective in |
| basis for taking operational, tactical and strategic | | | | achieving improvement goals. For improvement |
| decisions. | | | | efforts to succeed, senior management must |
| The main factors that render low quality to data | | | | recognize and adopt data quality as a key |
| are: | | | | business priority. |