Advertisment

Enterprise spend on BI tools to scale up

author-image
CIOL Bureau
Updated On
New Update

Increasing budgets for mature business intelligence (BI)

technologies (extraction, transformation and loading (ETL),

online analytical processing (OLAP)) may not herald an economic turn around, but

they are an
indication that

practitioners see the need to integrate and analyze data as being strong,

perhaps stronger even
than the

need to cut costs.

Advertisment

The Data Warehouse

Institute (TDWI) Giga Research Quarterly Technology
Survey,

conducted at the TDWI Boston Conference on August 25, 2003, has surfaced

indications of
increased optimism

in the business intelligence market. The data warehousing practitioners that

answered the
survey anticipated

spending more for mature BI technologies ETL (62 percent) and OLAP (60 percent),

as
well as servers in support of

data warehousing (62 percent). More modest increases for the less mature BI


technologies data mining (34 percent)

and data profiling (38 percent) point to a continued sense of caution
with

these useful, yet less proven emerging technologies.

There are other indicators hidden within these survey results,

as the largest segment of respondents remain undecided,

ranging from a low of 31percent for servers up to 63 percent for data mining.

However, there is
more optimism

in the numbers for increases greater than 5 percent, which are as high as 39

percent for ETL,
40 percent for

OLAP and 44 percent for servers. In contrast, the expected budget decreases are

an order of
magnitude lower for

all of these BI technologies, ranging from a low of 2.5 percent for OLAP to a

high of
only 6.2 percent for

servers (see table below).

These results should not be seen as a sign of complete economic

turnaround in light of the fact that there is no mention

of new jobs being created. Rather, these results should be viewed merely as a

subtle shift in the
perspective

of BI practitioners.

Advertisment

Recommendations

Advertisment

User organizations, and in particular IT, should increase

budgets for mature BI technologies, specifically data integration

and servers in support of BI initiatives. Approach emerging data mining

technologies cautiously,
placing

an emphasis on predictive analysis solutions (fraud detection, customer churn)

that are targeted at
business and

casual users rather than tools that are targeted at the statisticians. Look to

data quality as a major
technology

investment in the next 24 to 36 months, since bad data is more difficult to

integrate, wastes server
resources

and yields invalid historical and predictive analytic results
.

 

tech-news