In today’s data-rich world, the ability to extract meaningful insights from your information is critical for success. However, many organizations struggle to harness the full potential of their data due to a lack of clear strategy, fragmented data sources, and limited analytical capabilities.
Oblytech's Data Strategy & Consulting services help you overcome these challenges and get the most value from your data. Our consultants work closely with you to develop a comprehensive data strategy that aligns with your business objectives, identifies key performance indicators (KPIs), and outlines a roadmap for achieving data-driven success.
We provide training and support to help your team use data effectively and make data-driven decisions.
We help you visualize your data through interactive dashboards and reports, making it easier to understand and act upon insights.
We assist you in selecting and implementing the right data analytics tools and technologies to support your strategy.
We help you establish a solid data governance framework to ensure data quality, security, and compliance.
We create a roadmap that outlines the steps required to implement your data strategy, including technology recommendations, resource allocation, and timelines.
We collaborate with you to define your data goals, identify key metrics, and develop a comprehensive data strategy that aligns with your overall business objectives.
We conduct a thorough assessment of your current data landscape, including data sources, quality, accessibility, and governance. This helps us identify strengths, weaknesses, and areas for improvement.
Data strategy work is usually requested by a leadership team that has already spent money on data and is not sure what it bought. There may be a warehouse nobody trusts, a set of dashboards that no two directors read the same way, or a list of analytics requests that grows every quarter without a clear order. The person who sponsors the work is often a chief operating officer, a finance director or a newly appointed head of data. What they need is a way to decide what to do next, and in what order, with enough confidence to commit budget.
A strategy engagement is the right route when the problem is one of prioritization and definition rather than of technology. It is the wrong route when the business already knows its measures, owns its data and needs a specific pipeline or report built. In that case the work belongs on the engineering or reporting routes. This page describes what a useful strategy produces, how it is built and what it cannot settle on its own.
A strategy that ends in a presentation with a maturity model and a list of aspirations has not done its job. A useful strategy produces decisions the business can act on, and each decision should name an owner, a measure and a first step.
| Output | Question it answers | How to judge whether it is good |
|---|---|---|
| Priority decisions register | Which decisions will data change, and who makes them? | Each entry names a decision, a decision-maker and the frequency of the decision |
| Measure definitions | What exactly does each core number mean? | Two people in different functions would calculate the same figure from the same definition |
| Source ownership map | Which system is the authority for each record, and who maintains it? | Every shared record type has one owner and one stated source |
| Sequenced roadmap | What is built first, and what must be true before the next step? | Dependencies between steps are explicit and each step has an exit test |
| Governance model | How are definitions changed, and how are disputes settled? | A change log exists and a named forum decides disagreements |
The measure definitions are the output that most often reveals the real problem. A finance team and a sales team may both report revenue, and the strategy has to decide whether the difference is a definition error, a timing difference or a true disagreement about what counts. Discovering that early is far cheaper than discovering it after a dashboard is live.
A workable approach moves from the decisions outward. It starts with a small set of leaders who describe the decisions they make each month and the information they wish they had. From there the work maps each decision to the data it depends on, the source of that data and the person who maintains it. The current state is then assessed against those needs, covering data sources, quality, accessibility and governance. The assessment matters because it separates the gaps that block decisions from the gaps that are merely untidy.
The roadmap is the last step, and it is only as good as the assessment behind it. Each step should state the technology required, the people needed, the time it is expected to take and what it will change. Where the roadmap names a technology, it should justify the choice by the decision it serves, not by the features it offers.
The most common failure is a strategy that describes the future state in general terms and never commits to a first build. Leaders then have a document and no change. The fix is to require at least one early step that produces a visible result within the first stage of the roadmap, such as a single measure defined and agreed across two functions.
A second failure is ignoring the organization’s capacity to change. A strategy that assumes a data team can be hired quickly, or that business owners will spend time on definitions, is a plan for a different company. The roadmap should say who does the work, and how much of their time it needs.
A third failure is treating governance as a separate exercise. Governance decides who may change a definition and how a dispute is settled. Without it, the strategy’s definitions will be overwritten by whichever team needs a different number next quarter.
A strategy sets the order of work. Its outputs become the requirements for any warehousing and business intelligence work, and the source map tells whoever builds the pipelines which flows to take first. Where the strategy identifies a measure that depends on predictive analysis, AI use-case discovery is the related topic for judging whether that measure justifies a model, and AI value measurement is the related topic for how the value of a measure is judged. Where the measures sit in Salesforce, Salesforce strategy and roadmap development is the related page.
Strategy outputs are also the basis for the governance discussion that follows. A useful test is whether a new analyst could read the register and the definitions and know which number to use for a board pack without asking a colleague. If the answer is no, the strategy has produced a plan for the data but not yet a shared language for it. Closing that gap is often a matter of a few focused sessions with the people who own each measure, rather than a large additional workstream. The decision register should also be reviewed at the same cadence as the business planning cycle, so that the priorities it records keep pace with the decisions leaders are actually making.
It depends on how many functions are involved and how many decisions are in scope. A narrow strategy for one function can be produced far faster than an enterprise one.
Usually it is better to have the decision register and measure definitions first. A platform chosen before those exist tends to be configured around whatever the first team asked for.
A leader who makes decisions that depend on data and who can settle disputes between functions. A strategy sponsored only by IT tends to produce a technical roadmap that the business does not adopt.
Count the decisions that now use agreed definitions, the number of disputes over core numbers that are settled through the governance route and whether the first sequenced build reached users on schedule.
A reasonable first move is to list the three to five decisions your leadership team makes most often and the numbers each one depends on. That list shows whether a strategy is the right next step and how wide it should be, and it gives any later conversation a concrete starting point.
Data strategy services settle which business decisions data must support, who owns each source and measure, and what gets built first. A good engagement ends with named owners, agreed definitions and a sequenced roadmap that commits to a first build. A maturity score and a list of aspirations do not count as a strategy.
Scope is driven by four things rather than by the size of the company: how many business functions must agree on definitions, how many source systems hold the same record type, how many definitions already conflict, and how many decision-makers need to sign off. Two functions and one trusted system need far less than finance, sales and operations each keeping their own version of revenue.
Judge a provider by what it asks you to commit to, not by the framework it shows. Put these questions to any provider before signing:
Warning signs are a roadmap that starts from a tool list instead of from decisions, and a deliverable that would read the same for any client. Once the roadmap exists, the build work follows on the warehousing and business intelligence and data engineering and pipeline development routes. Where the next step is a model, checking data before building on AI comes first.
A data strategy decides what to measure, who owns each source and in what order to build. Data engineering builds the pipelines and storage that deliver those measures. Strategy comes first when definitions or priorities are disputed, and engineering comes first when they are settled.
The decisions data must support, the definitions of core measures, the owner of each source, a sequenced roadmap and a way to settle disputes. Anything beyond that tends to be detail the business cannot act on in its first year.
Often yes, when leaders can agree on definitions and someone has time to run the work. An outside team helps most where functions disagree and need a neutral party to record decisions, or where nobody can spare the hours.
Guides from the Oblytech ServiceNow library that explain the practice behind this page.