Top Five BI/Data Analytics Trends

As more organizations start digital transformation journey, data becomes lifeblood for modern businesses, BI/Data Analytics also becomes one of top priorities at CIO’s investment list. So what are top analytics trends in the upcoming year?

1. The Main Business Purposes of Data Analytics

Via one of recently released IW industry survey, companies of all sizes are embracing data visualization, self- service, BI and big data analysis. Advanced analytics is of particular interest to the respondents:

1)     62% say they’re using these technologies to optimize business operations;

2)     While 44% aim to identify business risk;
3)     And another 44% hope to predict the promising new business opportunities.

2. Experiment vs. Standardization

• Declining interest in Standardization: Among the majority of survey respondents at organizations using or planning to use data analytics, BI or statistical analysis software, the result shows declining interest in standardization;

• And companies experiment with niche vendors specializing in data visualization, self-service BI and big data analysis. More survey respondents say they’re using or planning to use products from these smaller, more innovative companies compared with last year. There was also an up tick in the percentage of respondents using or planning to use open source vendors.

• Cloud-based BI and performance management vendors are also gaining adoption

3.    Advanced Analytics is On Top 

A keen interest in advanced analytics is the one constant being seen across all five years of analytics and BI surveys. Advanced analytics is all about statistical analysis and predictive modeling — being able to see what’s coming and take action before it’s too late, it’s progressive move from traditional BI’s “rear mirror” analytics approach.

• Advanced analytics was the top choice in every survey. Advanced data visualization is No. 2 this year, up from being ranked third in 2009. Last year the survey added “big data analysis” to the list of cutting-edge pursuits, and this year it’s ranked No. 4 along with collaborative BI.

• In-database analysis for predictive or statistical modeling, to the list of leading-edge technologies, and respondents rated their interest higher. It’s the statistical and predictive algorithms are rewritten to operate inside databases that run on massively parallel processing (MPP) platforms. In-database analysis is faster than the old approach.

• Text mining: In-database approaches are maturing and breaking into new areas. Text mining, which is applied to unstructured text, is becoming a more popular advanced analytics technique

• Crowd-sourcing Analysis: Businesses understand it’s important for them to be able to do data mining to pull data together from divergent systems and begin to answer a variety of operational questions and it gives advanced analytics practitioners plenty of ammunition for their predictive models.

4.    The Big Data Ambitions & Movement 

Big Data is about embracing new data types, like social media, click streams and log files, low-latency data, sensor information and other real-time feeds.

The good news for those who want to predict rather than just report history is that corporate data stores are rapidly growing in volume, variety and complexity. That’s abundance of big data, the bad news is Big data expertise is scarce and expensive, and Data warehouse appliance platforms are also expensive.

(1) Big Data Ambitions

What’s driving interest in big data analysis? Survey respondents say

·      “finding correlations across multiple, disparate data source,”

·      “predicting customer behavior”

·       and “predicting product or service sales” are the top drivers.

(2) Big concerns about big data analytics

• Lack of clear business case: Not so sure how Big Data will create Business Value and opportunities;
• Big Data Expertise is scarce
• DW appliance platform are expensive
• Analytical tools are lacking for big data platforms like Hadoop and NoSQL databases
• Hadoop and other NoSQL technologies lack management features
• and new technologies such as Hadoop and NoSQL are hard to learn.
• Don’t have enough Data or Data quality concerns.

5.    Most Important Analytics/BI Implementation Goal & Feature

(1) BI Implementation Goal:

• Improve business planning
• Provide business reporting tools
• Enable real-time information
• Monitor and share business performance metrics
• Share information with executives

(2) Ease of implementation

• Provides fast data exploration, query and analysis capabilities
• Ability for users to share and collaborate on information
• Ability to collect and analyze operational data in real time
• Ease of use for a broad range of workers

Posted in: Big Data,Business Intelligence