An occasional series in which a review of recent posts on SmartData Collective reveals the following nuggets:
First, the decision
The first step is to focus BI and dashboard development on decisions, and this means identifying the decisions that your users must make, the questions to which they need answers, and then focusing on these like a laser. Data quality, data integration, timeliness should all be driven by the decision’s needs. This can be a challenge for companies and is one I often spend a lot of time on when I work to help them improve their use of analytics.
Communicate it right
When you present the business case for your data quality initiative to
executive management and other corporate stakeholders, remember the
lessons of show and tell. Poor data quality is not a theoretical
problem – it is a real business problem that negatively impacts the
quality of decision-critical enterprise information. Your presentation
should make it clear that if the data quality initiative doesn’t get
approved, then everyone will know exactly what to expect:
Tapping into social concepts and tools
What if the current business transformation follows the same path as ERP / BPE and CRM? One could argue that we have already created mountains of social data as a result of web 2.0 social networking, blogging, social bookmarking, and micro blogging. After all, one of the key principals of web 2.0 is transparency, which has lead to much more openness in our life “mash ups” that merge our personal and professional lives online. As businesses implement the use of social tools (or I should say continue to implement social tools — all businesses at least have email, even if it’s a consumer provided alternative like Gmail or Yahoo that has been generating social data for years), I’ve been thinking about how the social concepts and tools move into the enterprise and more importantly how we create scalable, enterprise-class processes to support this.
From monologue to conversation
Blogging, at least for me, is about public conversation. It’s an asymmetric conversation, for sure: I’m the blogger (at least on my blog!), so I get to go first. But my best posts — and, in my opinion, the best blog posts in general — are those where a comment stream quickly takes over, making the post more of a conversation starter than a monologue. Perhaps the best example of this on my blog is “Looking for a Devil’s Advocate“, which inspired over sixty comments in a conversation that lasted for three weeks.
So what do we mean?
It occurs to me that this foofaraw is caused in some part by language. Charts, visualizations, graphics – they all have different purposes in different venues: sometimes to present data, sometimes to explore and make discoveries, sometimes to offer an analysis or conclusion, sometimes to communicate a story. And sometimes they are by design not science but art, or intended “merely” to entertain. Yet we have no simple way to categorize these uses: as I just demonstrated, the words “chart”, “visualization”, “graphic” and others are all used interchangeably.
Get focused
Where marketing fails is that customers are focused on their business, not yours. Conversations in social media marketing today are still more focused on “Look at me, Mr. Customer!” All the customer wants is for you to look at them. It is an effort for customers to utilize and participate in social networks and gather information in social media. There are still too many places the customer has to go to interact. We make it difficult to solidify relationships by managing multiple properties and outlets to connect.
Standardizing data
Most CIOs don’t recognize that they have a data supply chain. Instead of building a custom distribution system for each suppler (each business application), they should be focused on a single data supply chain. Middleware supports the creation of custom distribution solutions, but not the standardization of data. A data supply chain can only be successful if the data is standardized. Otherwise, everyone is forced to write custom code to standardize, clean, and integrate the data.
